diff --git a/pyproject.toml b/pyproject.toml index 5e111f8..1127efb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,12 @@ description = "Compiled UK tax-benefit microsimulation engine (Rust binary with requires-python = ">=3.10" license = {text = "MIT"} dependencies = [ + "odfpy>=1.4.1", + "openpyxl>=3.1.5", + "pandas>=2.3.3", "pydantic>=2.0", + "requests>=2.33.1", + "rich>=14.3.3", ] [project.urls] diff --git a/scripts/build_targets/__init__.py b/scripts/build_targets/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/scripts/build_targets/dwp.py b/scripts/build_targets/dwp.py new file mode 100644 index 0000000..05c3a58 --- /dev/null +++ b/scripts/build_targets/dwp.py @@ -0,0 +1,395 @@ +"""Fetch DWP benefit statistics from the Stat-Xplore API. + +Queries caseloads for UC (with subgroup breakdowns), PIP, pension credit, +carer's allowance, attendance allowance, state pension, ESA, and DLA. +Results are cached locally to avoid repeated API calls. + +Requires STAT_XPLORE_API_KEY environment variable to be set. +See: https://stat-xplore.dwp.gov.uk/webapi/online-help/Open-Data-API.html +""" + +from __future__ import annotations + +import json +import logging +import os +from pathlib import Path + +import requests + +logger = logging.getLogger(__name__) + +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +CACHE_DIR = REPO_ROOT / "data" / "cache" +CACHE_FILE = CACHE_DIR / "dwp_stat_xplore.json" + +API_BASE = "https://stat-xplore.dwp.gov.uk/webapi/rest/v1" +API_KEY = os.environ.get("STAT_XPLORE_API_KEY", "") + + +def _headers() -> dict: + return {"apiKey": API_KEY, "Content-Type": "application/json"} + + +def _query_table( + database: str, + measures: list[str], + dimensions: list[list[str]], +) -> dict: + """Send a table query to stat-xplore and return the JSON response.""" + payload: dict = { + "database": database, + "measures": measures, + "dimensions": dimensions, + } + r = requests.post(f"{API_BASE}/table", headers=_headers(), json=payload, timeout=30) + r.raise_for_status() + return r.json() + + +def _extract_year(result: dict) -> int: + """Extract the year from the auto-selected date field.""" + for field in result.get("fields", []): + for item in field.get("items", []): + for label in item.get("labels", []): + for part in str(label).replace("-", " ").split(): + if part.isdigit(): + y = int(part) + return y if y > 100 else 2000 + y + return 2025 + + +def _extract_total(result: dict) -> float | None: + """Extract the single value from a no-dimension query.""" + cubes = result.get("cubes", {}) + if not cubes: + return None + values = next(iter(cubes.values()))["values"] + # Unwrap nested lists (stat-xplore wraps in [date][value]) + while isinstance(values, list) and len(values) == 1: + values = values[0] + return values if isinstance(values, (int, float)) else None + + +def _extract_breakdown(result: dict) -> list[tuple[str, float]]: + """Extract label/value pairs from a single-dimension query. + + Stat-xplore auto-adds the date dimension, so the response has two fields: + date (1 item = latest month) and the requested dimension (N items). + Values are shaped [1][N]. + """ + fields = result.get("fields", []) + cubes = result.get("cubes", {}) + if not cubes: + return [] + vals = next(iter(cubes.values()))["values"] + + # Find the non-date dimension + dim_field = None + for f in fields: + if "month" not in f["label"].lower() and "date" not in f["label"].lower(): + dim_field = f + break + if dim_field is None: + return [] + + items = dim_field["items"] + # Values are [date_idx][dim_idx] — take last date row + row = vals[-1] if isinstance(vals[0], list) else vals + pairs = [] + for i, item in enumerate(items): + v = row[i] if isinstance(row, list) else row + if v is not None and v > 0: + pairs.append((item["labels"][0], float(v))) + return pairs + + +# ── Simple total caseload queries ────────────────────────────────────────── + + +# (database, measure, target_name, survey_variable, entity) +_SIMPLE_BENEFITS = [ + ( + "str:database:UC_Monthly", + "str:count:UC_Monthly:V_F_UC_CASELOAD_FULL", + "dwp/uc_total_claimants", + "universal_credit", + "person", + ), + ( + "str:database:PIP_Monthly_new", + "str:count:PIP_Monthly_new:V_F_PIP_MONTHLY", + "dwp/pip_total_claimants", + "pip_daily_living", + "person", + ), + ( + "str:database:PC_New", + "str:count:PC_New:V_F_PC_CASELOAD_New", + "dwp/pension_credit_claimants", + "pension_credit", + "person", + ), + ( + "str:database:CA_In_Payment_New", + "str:count:CA_In_Payment_New:V_F_CA_In_Payment_New", + "dwp/carers_allowance_claimants", + "carers_allowance", + "person", + ), + ( + "str:database:AA_In_Payment_New", + "str:count:AA_In_Payment_New:V_F_AA_In_Payment_New", + "dwp/attendance_allowance_claimants", + "attendance_allowance", + "person", + ), + ( + "str:database:SP_New", + "str:count:SP_New:V_F_SP_CASELOAD_New", + "dwp/state_pension_claimants", + "state_pension", + "person", + ), + ( + "str:database:ESA_Caseload_new", + "str:count:ESA_Caseload_new:V_F_ESA_NEW", + "dwp/esa_claimants", + "esa_income", + "person", + ), + ( + "str:database:DLA_In_Payment_New", + "str:count:DLA_In_Payment_New:V_F_DLA_In_Payment_New", + "dwp/dla_claimants", + "dla_care", + "person", + ), +] + + +def _fetch_simple_benefits() -> list[dict]: + """Fetch total caseload for each benefit.""" + targets = [] + for database, measure, name, variable, entity in _SIMPLE_BENEFITS: + try: + result = _query_table(database, [measure], []) + total = _extract_total(result) + if total is not None: + year = _extract_year(result) + targets.append( + { + "name": name, + "variable": variable, + "entity": entity, + "aggregation": "count_nonzero", + "filter": None, + "value": total, + "source": "dwp", + "year": year, + "holdout": False, + } + ) + except Exception as e: + logger.warning("Failed to fetch %s: %s", name, e) + return targets + + +# ── UC subgroup breakdowns (households) ──────────────────────────────────── + +_UC_HH_DB = "str:database:UC_Households" +_UC_HH_COUNT = "str:count:UC_Households:V_F_UC_HOUSEHOLDS" +_UC_HH_FIELD = "str:field:UC_Households:V_F_UC_HOUSEHOLDS" + + +def _fetch_uc_breakdowns() -> list[dict]: + """Fetch UC household breakdowns by family type, entitlement elements, etc.""" + targets = [] + + # UC households by family type — map to benunit_filter conditions + try: + result = _query_table( + _UC_HH_DB, + [_UC_HH_COUNT], + [[f"{_UC_HH_FIELD}:hnfamily_type"]], + ) + year = _extract_year(result) + for label, value in _extract_breakdown(result): + slug = label.lower().replace(",", "").replace(" ", "_") + if "unknown" in slug or "missing" in slug: + continue + # Map family type labels to benunit filter conditions + bf = {} + if "single" in slug and "no_child" in slug: + bf = {"is_couple": False, "has_children": False} + elif "single" in slug and "child" in slug: + bf = {"is_couple": False, "has_children": True} + elif "couple" in slug and "no_child" in slug: + bf = {"is_couple": True, "has_children": False} + elif "couple" in slug and "child" in slug: + bf = {"is_couple": True, "has_children": True} + + targets.append( + { + "name": f"dwp/uc_households_{slug}", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": bf if bf else None, + "value": value, + "source": "dwp", + "year": year, + "holdout": True, + } + ) + except Exception as e: + logger.warning("Failed to fetch UC family type breakdown: %s", e) + + # UC households with child entitlement + try: + result = _query_table( + _UC_HH_DB, + [_UC_HH_COUNT], + [[f"{_UC_HH_FIELD}:HCCHILD_ENTITLEMENT"]], + ) + year = _extract_year(result) + for label, value in _extract_breakdown(result): + if label.lower() == "yes": + targets.append( + { + "name": "dwp/uc_households_with_children", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": {"has_children": True}, + "value": value, + "source": "dwp", + "year": year, + "holdout": False, + } + ) + except Exception as e: + logger.warning("Failed to fetch UC child entitlement breakdown: %s", e) + + # UC households with LCWRA entitlement (disability element) + try: + result = _query_table( + _UC_HH_DB, + [_UC_HH_COUNT], + [[f"{_UC_HH_FIELD}:HCLCW_ENTITLEMENT"]], + ) + year = _extract_year(result) + for label, value in _extract_breakdown(result): + slug = label.lower().replace(" ", "_").replace("/", "_") + if slug == "lcwra": + targets.append( + { + "name": "dwp/uc_households_lcwra", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": {"has_lcwra": True}, + "value": value, + "source": "dwp", + "year": year, + "holdout": False, + } + ) + elif slug == "lcw": + targets.append( + { + "name": "dwp/uc_households_lcw", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": {"has_lcw": True}, + "value": value, + "source": "dwp", + "year": year, + "holdout": True, + } + ) + except Exception as e: + logger.warning("Failed to fetch UC LCW breakdown: %s", e) + + # UC households with carer entitlement + try: + result = _query_table( + _UC_HH_DB, + [_UC_HH_COUNT], + [[f"{_UC_HH_FIELD}:HCCARER_ENTITLEMENT"]], + ) + year = _extract_year(result) + for label, value in _extract_breakdown(result): + if label.lower() == "yes": + targets.append( + { + "name": "dwp/uc_households_with_carer", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": {"has_carer": True}, + "value": value, + "source": "dwp", + "year": year, + "holdout": True, + } + ) + except Exception as e: + logger.warning("Failed to fetch UC carer breakdown: %s", e) + + # UC households with housing entitlement + try: + result = _query_table( + _UC_HH_DB, + [_UC_HH_COUNT], + [[f"{_UC_HH_FIELD}:TENURE"]], + ) + year = _extract_year(result) + for label, value in _extract_breakdown(result): + if label.lower() == "yes": + targets.append( + { + "name": "dwp/uc_households_with_housing", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "count_nonzero", + "filter": None, + "benunit_filter": {"has_housing": True}, + "value": value, + "source": "dwp", + "year": year, + "holdout": False, + } + ) + except Exception as e: + logger.warning("Failed to fetch UC housing breakdown: %s", e) + + return targets + + +def get_targets() -> list[dict]: + if CACHE_FILE.exists(): + logger.info("Using cached DWP targets: %s", CACHE_FILE) + return json.loads(CACHE_FILE.read_text()) + + if not API_KEY: + logger.warning( + "STAT_XPLORE_API_KEY not set — skipping DWP targets. " + "Set the env var and re-run to fetch from stat-xplore." + ) + return [] + + targets = [] + targets.extend(_fetch_simple_benefits()) + targets.extend(_fetch_uc_breakdowns()) + + CACHE_DIR.mkdir(parents=True, exist_ok=True) + CACHE_FILE.write_text(json.dumps(targets, indent=2)) + logger.info("Cached %d DWP targets to %s", len(targets), CACHE_FILE) + return targets diff --git a/scripts/build_targets/hmrc.py b/scripts/build_targets/hmrc.py new file mode 100644 index 0000000..8b5182d --- /dev/null +++ b/scripts/build_targets/hmrc.py @@ -0,0 +1,196 @@ +"""Parse HMRC Survey of Personal Incomes into calibration targets. + +Downloads the SPI collated ODS from gov.uk (Tables 3.6 and 3.7) and builds +income-by-band targets for employment, self-employment, pensions, property, +dividends, and savings interest — both amounts and taxpayer counts per band. + +Source: https://www.gov.uk/government/statistics/income-tax-summarised-accounts-statistics +""" + +from __future__ import annotations + +import io +import logging +from functools import lru_cache +from pathlib import Path + +import pandas as pd +import requests + +logger = logging.getLogger(__name__) + +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +CACHE_DIR = REPO_ROOT / "data" / "cache" + +# HMRC SPI 2022-23 collated tables (ODS) +SPI_URL = "https://assets.publishing.service.gov.uk/media/67cabb37ade26736dbf9ffe5/Collated_Tables_3_1_to_3_17_2223.ods" +SPI_YEAR = 2023 # FY 2022-23 → calendar 2023 + +INCOME_BANDS_LOWER = [ + 12_570, + 15_000, + 20_000, + 30_000, + 40_000, + 50_000, + 70_000, + 100_000, + 150_000, + 200_000, + 300_000, + 500_000, + 1_000_000, +] +INCOME_BANDS_UPPER = INCOME_BANDS_LOWER[1:] + [ + 1e12 +] # Effectively infinity, but JSON-safe + +INCOME_VARIABLES = [ + "employment_income", + "self_employment_income", + "state_pension", + "private_pension_income", + "property_income", + "dividend_income", +] + + +def _to_float(val) -> float: + if val is None: + return 0.0 + if isinstance(val, (int, float)): + return float(val) + s = str(val).replace(",", "").replace("£", "").strip() + if s in ("", "-", "..", ".."): + return 0.0 + return float(s) + + +@lru_cache(maxsize=1) +def _download_ods() -> bytes: + cache_file = CACHE_DIR / "hmrc_spi.ods" + if cache_file.exists(): + logger.info("Using cached HMRC SPI ODS: %s", cache_file) + return cache_file.read_bytes() + + logger.info("Downloading HMRC SPI ODS...") + r = requests.get(SPI_URL, timeout=60, allow_redirects=True) + r.raise_for_status() + CACHE_DIR.mkdir(parents=True, exist_ok=True) + cache_file.write_bytes(r.content) + return r.content + + +def _parse_table_36(ods_bytes: bytes) -> pd.DataFrame: + """Table 3.6: employment, self-employment, state/private pensions by income band.""" + df = pd.read_excel( + io.BytesIO(ods_bytes), sheet_name="Table_3_6", engine="odf", header=None + ) + rows = [] + for i in range(5, len(df)): + lower = df.iloc[i, 0] + if not isinstance(lower, (int, float)): + break + rows.append( + { + "lower_bound": int(lower), + "self_employment_income_count": _to_float(df.iloc[i, 1]), + "self_employment_income_amount": _to_float(df.iloc[i, 2]), + "employment_income_count": _to_float(df.iloc[i, 4]), + "employment_income_amount": _to_float(df.iloc[i, 5]), + "state_pension_count": _to_float(df.iloc[i, 7]), + "state_pension_amount": _to_float(df.iloc[i, 8]), + "private_pension_income_count": _to_float(df.iloc[i, 10]), + "private_pension_income_amount": _to_float(df.iloc[i, 11]), + } + ) + return pd.DataFrame(rows) + + +def _parse_table_37(ods_bytes: bytes) -> pd.DataFrame: + """Table 3.7: property, savings interest, dividends by income band.""" + df = pd.read_excel( + io.BytesIO(ods_bytes), sheet_name="Table_3_7", engine="odf", header=None + ) + rows = [] + for i in range(5, len(df)): + lower = df.iloc[i, 0] + if not isinstance(lower, (int, float)): + break + rows.append( + { + "lower_bound": int(lower), + "property_income_count": _to_float(df.iloc[i, 1]), + "property_income_amount": _to_float(df.iloc[i, 2]), + "savings_interest_count": _to_float(df.iloc[i, 4]), + "savings_interest_amount": _to_float(df.iloc[i, 5]), + "dividend_income_count": _to_float(df.iloc[i, 7]), + "dividend_income_amount": _to_float(df.iloc[i, 8]), + } + ) + return pd.DataFrame(rows) + + +def get_targets() -> list[dict]: + targets = [] + try: + ods_bytes = _download_ods() + except Exception as e: + logger.error("Failed to download HMRC SPI ODS: %s", e) + return targets + + t36 = _parse_table_36(ods_bytes) + t37 = _parse_table_37(ods_bytes) + merged = t36.merge(t37, on="lower_bound", how="outer") + + # Hold out count targets as validation (amounts used for training) + for idx, row in merged.iterrows(): + lower = int(row["lower_bound"]) + upper = INCOME_BANDS_UPPER[idx] if idx < len(INCOME_BANDS_UPPER) else 1e12 + band_label = f"{lower}_to_{upper:.0f}" if upper < 1e12 else f"{lower}_plus" + + for variable in INCOME_VARIABLES: + amount_col = f"{variable}_amount" + count_col = f"{variable}_count" + + if amount_col in row.index and row[amount_col] > 0: + # SPI amounts are in £millions + targets.append( + { + "name": f"hmrc/{variable}_amount_{band_label}", + "variable": variable, + "entity": "person", + "aggregation": "sum", + "filter": { + "variable": "total_income", + "min": float(lower), + "max": float(upper), + }, + "value": float(row[amount_col]) * 1e6, + "source": "hmrc_spi", + "year": SPI_YEAR, + "holdout": False, + } + ) + + if count_col in row.index and row[count_col] > 0: + # SPI counts are in thousands — use as holdout validation + targets.append( + { + "name": f"hmrc/{variable}_count_{band_label}", + "variable": variable, + "entity": "person", + "aggregation": "count_nonzero", + "filter": { + "variable": "total_income", + "min": float(lower), + "max": float(upper), + }, + "value": float(row[count_col]) * 1e3, + "source": "hmrc_spi", + "year": SPI_YEAR, + "holdout": True, + } + ) + + return targets diff --git a/scripts/build_targets/main.py b/scripts/build_targets/main.py new file mode 100644 index 0000000..01daee7 --- /dev/null +++ b/scripts/build_targets/main.py @@ -0,0 +1,112 @@ +"""Build calibration targets from all sources and write to JSON. + +Usage: + python scripts/build_targets/main.py # build all targets + python scripts/build_targets/main.py --year 2025 # filter to a specific year + python scripts/build_targets/main.py --clear-cache # re-fetch cached data + python scripts/build_targets/main.py --output out.json # custom output path +""" + +from __future__ import annotations + +import argparse +import json +import logging +import shutil +import sys +from pathlib import Path + +from rich.console import Console +from rich.table import Table + +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +sys.path.insert(0, str(REPO_ROOT / "scripts")) + +from build_targets import obr, hmrc, dwp, ons # noqa: E402 + +logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") +logger = logging.getLogger(__name__) + +DEFAULT_OUTPUT = REPO_ROOT / "data" / "calibration_targets.json" +CACHE_DIR = REPO_ROOT / "data" / "cache" + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__ or "") + parser.add_argument("--year", type=int, help="Only include targets for this year") + parser.add_argument( + "--output", type=Path, default=DEFAULT_OUTPUT, help="Output JSON path" + ) + parser.add_argument( + "--clear-cache", + action="store_true", + help="Clear cached downloads before building", + ) + args = parser.parse_args() + + if args.clear_cache and CACHE_DIR.exists(): + shutil.rmtree(CACHE_DIR) + logger.info("Cleared cache directory") + + console = Console() + all_targets: list[dict] = [] + sources = [ + ("OBR", obr.get_targets), + ("HMRC", hmrc.get_targets), + ("DWP", dwp.get_targets), + ("ONS", ons.get_targets), + ] + + for name, getter in sources: + try: + targets = getter() + all_targets.extend(targets) + logger.info("%s: %d targets", name, len(targets)) + except Exception as e: + logger.error("%s: failed — %s", name, e) + + # Filter by year if requested + if args.year: + all_targets = [t for t in all_targets if t["year"] == args.year] + + # Deduplicate by name + seen: dict[str, dict] = {} + for t in all_targets: + seen[t["name"]] = t + all_targets = list(seen.values()) + + # Write output + args.output.parent.mkdir(parents=True, exist_ok=True) + with open(args.output, "w") as f: + json.dump({"targets": all_targets}, f, indent=2) + + # Summary table + table = Table(title="Calibration targets") + table.add_column("Source") + table.add_column("Count", justify="right") + table.add_column("Training", justify="right") + table.add_column("Holdout", justify="right") + + by_source: dict[str, list[dict]] = {} + for t in all_targets: + by_source.setdefault(t["source"], []).append(t) + + for source, targets in sorted(by_source.items()): + training = sum(1 for t in targets if not t.get("holdout")) + holdout = sum(1 for t in targets if t.get("holdout")) + table.add_row(source, str(len(targets)), str(training), str(holdout)) + + table.add_row( + "Total", + str(len(all_targets)), + str(sum(1 for t in all_targets if not t.get("holdout"))), + str(sum(1 for t in all_targets if t.get("holdout"))), + style="bold", + ) + + console.print(table) + console.print(f"\nWrote {len(all_targets)} targets to {args.output}") + + +if __name__ == "__main__": + main() diff --git a/scripts/build_targets/obr.py b/scripts/build_targets/obr.py new file mode 100644 index 0000000..065a2de --- /dev/null +++ b/scripts/build_targets/obr.py @@ -0,0 +1,327 @@ +"""Parse OBR EFO March 2026 detailed forecast tables into calibration targets. + +Sources (local xlsx files in data/obr/): +- Receipts: efo-march-2026-detailed-forecast-tables-receipts.xlsx +- Expenditure: efo-march-2026-detailed-forecast-tables-expenditure.xlsx +""" + +from __future__ import annotations + +from pathlib import Path + +import openpyxl + +REPO_ROOT = Path(__file__).resolve().parent.parent.parent +OBR_DIR = REPO_ROOT / "data" / "obr" + +RECEIPTS_FILE = OBR_DIR / "efo-march-2026-detailed-forecast-tables-receipts.xlsx" +EXPENDITURE_FILE = OBR_DIR / "efo-march-2026-detailed-forecast-tables-expenditure.xlsx" + +# Sheet 3.8 (cash receipts): D=2024-25, E=2025-26, ..., J=2030-31 +_RECEIPTS_COL_TO_YEAR = { + "D": 2024, + "E": 2025, + "F": 2026, + "G": 2027, + "H": 2028, + "I": 2029, + "J": 2030, +} + +# Sheet 4.9 (welfare): C=2024-25, D=2025-26, ..., I=2030-31 +_WELFARE_COL_TO_YEAR = { + "C": 2024, + "D": 2025, + "E": 2026, + "F": 2027, + "G": 2028, + "H": 2029, + "I": 2030, +} + +# Sheet 4.1 (council tax): same layout as 4.9 +_CT_COL_TO_YEAR = _WELFARE_COL_TO_YEAR + +# Sheet 3.4 (IT + NICs detail): C=2024-25, ..., I=2030-31 +_IT_COL_TO_YEAR = { + "C": 2024, + "D": 2025, + "E": 2026, + "F": 2027, + "G": 2028, + "H": 2029, + "I": 2030, +} + + +def _find_row(ws, label: str, col: str = "B", max_row: int = 70) -> int | None: + for row in range(1, max_row + 1): + val = ws[f"{col}{row}"].value + if val and str(val).strip().startswith(label): + return row + return None + + +def _read_row(ws, row: int, col_map: dict[str, int]) -> dict[int, float]: + result = {} + for col, year in col_map.items(): + val = ws[f"{col}{row}"].value + if val is not None and isinstance(val, (int, float)): + result[year] = float(val) * 1e9 # £bn → £ + return result + + +def _parse_receipts() -> list[dict]: + """Parse sheet 3.8 for main tax receipts.""" + wb = openpyxl.load_workbook(RECEIPTS_FILE, data_only=True) + ws = wb["3.8"] + targets = [] + + # Map: (label_prefix, target_name, variable in the survey data, entity, aggregation) + # These are aggregate £ totals. For calibration we map them to survey-reported + # income/benefit variables where possible. + # Now that calibration runs after simulation, we can use simulated output + # variables (income_tax, national_insurance, capital_gains_tax, etc.) + receipt_rows = [ + ( + "Income tax (gross of tax credits)", + "obr/income_tax_receipts", + "income_tax", + "person", + "sum", + "Simulated income tax", + ), + ( + "National insurance contributions", + "obr/ni_receipts", + "total_ni", + "person", + "sum", + "Simulated employee + employer NI", + ), + ( + "Value added tax", + "obr/vat_receipts", + "vat", + "household", + "sum", + "Simulated VAT", + ), + ("Fuel duties", "obr/fuel_duty_receipts", "fuel_duty", "household", "sum", ""), + ( + "Capital gains tax", + "obr/cgt_receipts", + "capital_gains_tax", + "household", + "sum", + "", + ), + ( + "Stamp duty land tax", + "obr/sdlt_receipts", + "stamp_duty", + "household", + "sum", + "", + ), + ( + "Council tax", + "obr/council_tax_receipts", + "council_tax_annual", + "household", + "sum", + "", + ), + ] + + for label, name, variable, entity, aggregation, _note in receipt_rows: + if variable is None: + continue # Skip targets we can't map to survey data + row = _find_row(ws, label) + if row is None: + continue + values = _read_row(ws, row, _RECEIPTS_COL_TO_YEAR) + for year, value in values.items(): + targets.append( + { + "name": f"{name}/{year}", + "variable": variable, + "entity": entity, + "aggregation": aggregation, + "filter": None, + "value": value, + "source": "obr", + "year": year, + "holdout": False, + } + ) + + wb.close() + return targets + + +def _parse_it_nics_detail() -> list[dict]: + """Parse sheet 3.4 for income tax and NICs breakdown.""" + wb = openpyxl.load_workbook(RECEIPTS_FILE, data_only=True) + ws = wb["3.4"] + targets = [] + + rows_to_parse = [ + ( + "Income tax (gross of tax credits)", + "obr/income_tax", + "income_tax", + "person", + "sum", + ), + ( + "Class 1 Employee NICs", + "obr/ni_employee", + "national_insurance", + "person", + "sum", + ), + ] + + for label, name, variable, entity, aggregation in rows_to_parse: + row = _find_row(ws, label) + if row is None: + continue + values = _read_row(ws, row, _IT_COL_TO_YEAR) + for year, value in values.items(): + targets.append( + { + "name": f"{name}/{year}", + "variable": variable, + "entity": entity, + "aggregation": aggregation, + "filter": None, + "value": value, + "source": "obr", + "year": year, + "holdout": False, + } + ) + + wb.close() + return targets + + +def _parse_welfare() -> list[dict]: + """Parse sheet 4.9 for benefit spending totals.""" + wb = openpyxl.load_workbook(EXPENDITURE_FILE, data_only=True) + ws = wb["4.9"] + targets = [] + + # Map OBR row labels to simulated benefit variables. + # Benefits are calculated at benunit level in the simulation. + benefit_rows = [ + ( + "Housing benefit (not on JSA)", + "obr/housing_benefit", + "housing_benefit", + "benunit", + ), + ( + "Disability living allowance and personal independence p", + "obr/pip_dla", + "pip_daily_living", + "person", # PIP/DLA are passthrough (input data), not simulated + ), + ( + "Attendance allowance", + "obr/attendance_allowance", + "attendance_allowance", + "person", # Passthrough + ), + ("Pension credit", "obr/pension_credit", "pension_credit", "benunit"), + ("Carer's allowance", "obr/carers_allowance", "carers_allowance", "benunit"), + ("Child benefit", "obr/child_benefit", "child_benefit", "benunit"), + ("State pension", "obr/state_pension", "state_pension", "benunit"), + ] + + # UC appears twice in 4.9 — inside and outside the welfare cap. We want both. + uc_rows_found = 0 + for row_num in range(6, 50): + val = ws[f"B{row_num}"].value + if val and str(val).strip().startswith("Universal credit"): + uc_rows_found += 1 + suffix = "in_cap" if uc_rows_found == 1 else "outside_cap" + values = _read_row(ws, row_num, _WELFARE_COL_TO_YEAR) + for year, value in values.items(): + targets.append( + { + "name": f"obr/universal_credit_{suffix}/{year}", + "variable": "universal_credit", + "entity": "benunit", + "aggregation": "sum", + "filter": None, + "value": value, + "source": "obr", + "year": year, + "holdout": suffix + == "outside_cap", # Only use one UC total for training + } + ) + + for label, name, variable, entity in benefit_rows: + row = _find_row(ws, label) + if row is None: + continue + values = _read_row(ws, row, _WELFARE_COL_TO_YEAR) + for year, value in values.items(): + targets.append( + { + "name": f"{name}/{year}", + "variable": variable, + "entity": entity, + "aggregation": "sum", + "filter": None, + "value": value, + "source": "obr", + "year": year, + "holdout": False, + } + ) + + wb.close() + return targets + + +def _parse_council_tax() -> list[dict]: + """Parse sheet 4.1 for council tax receipts.""" + wb = openpyxl.load_workbook(EXPENDITURE_FILE, data_only=True) + ws = wb["4.1"] + targets = [] + + row = _find_row(ws, "Total net council tax receipts") + if row: + values = _read_row(ws, row, _CT_COL_TO_YEAR) + for year, value in values.items(): + targets.append( + { + "name": f"obr/council_tax/{year}", + "variable": "council_tax_annual", + "entity": "household", + "aggregation": "sum", + "filter": None, + "value": value, + "source": "obr", + "year": year, + "holdout": False, + } + ) + + wb.close() + return targets + + +def get_targets() -> list[dict]: + targets = [] + if RECEIPTS_FILE.exists(): + targets.extend(_parse_receipts()) + targets.extend(_parse_it_nics_detail()) + if EXPENDITURE_FILE.exists(): + targets.extend(_parse_welfare()) + targets.extend(_parse_council_tax()) + return targets diff --git a/scripts/build_targets/ons.py b/scripts/build_targets/ons.py new file mode 100644 index 0000000..39462ef --- /dev/null +++ b/scripts/build_targets/ons.py @@ -0,0 +1,110 @@ +"""ONS demographic calibration targets. + +Population by age group, total households, and regional distribution. +These are from ONS mid-year population estimates and household projections. + +Sources: +- ONS mid-year population estimates 2023 +- ONS household projections +""" + +from __future__ import annotations + + +# ONS mid-2023 population estimates (UK), rounded to nearest 1000. +# Source: ONS MYE 2023. +_POPULATION = { + "children_0_15": 12_200_000, + "working_age_16_64": 42_100_000, + "pensioners_65_plus": 12_600_000, + "total": 66_900_000, +} + +# ONS household projections 2023 (England + Scotland + Wales + NI) +_TOTAL_HOUSEHOLDS = 28_200_000 + +# Regional population shares (2023 mid-year estimates) +# Regions match the FRS region codes. +_REGIONAL_POPULATION = { + "north_east": 2_650_000, + "north_west": 7_400_000, + "yorkshire": 5_550_000, + "east_midlands": 4_900_000, + "west_midlands": 5_950_000, + "east_of_england": 6_350_000, + "london": 8_800_000, + "south_east": 9_300_000, + "south_west": 5_700_000, + "wales": 3_100_000, + "scotland": 5_450_000, + "northern_ireland": 1_900_000, +} + + +def get_targets() -> list[dict]: + """Generate ONS demographic targets for all calibration years. + + Population changes slowly year-to-year, so we emit the same targets for + each year in the calibration range. This ensures they bind regardless of + which --year is passed to calibration. + """ + targets = [] + + # Emit for all plausible calibration years + for year in range(2024, 2031): + # Age group population counts + for group, count in _POPULATION.items(): + if group == "total": + continue + if group == "children_0_15": + age_filter = {"variable": "age", "min": 0, "max": 16} + elif group == "working_age_16_64": + age_filter = {"variable": "age", "min": 16, "max": 65} + else: # pensioners + age_filter = {"variable": "age", "min": 65, "max": 200} + + targets.append( + { + "name": f"ons/population_{group}/{year}", + "variable": "age", + "entity": "person", + "aggregation": "count", + "filter": age_filter, + "value": float(count), + "source": "ons", + "year": year, + "holdout": False, + } + ) + + # Total population + targets.append( + { + "name": f"ons/total_population/{year}", + "variable": "age", + "entity": "person", + "aggregation": "count", + "filter": None, + "value": float(_POPULATION["total"]), + "source": "ons", + "year": year, + "holdout": False, + } + ) + + # Total households + targets.append( + { + "name": f"ons/total_households/{year}", + "variable": "household_id", + "entity": "household", + "aggregation": "count", + "filter": None, + "value": float(_TOTAL_HOUSEHOLDS), + "source": "ons", + "year": year, + "holdout": False, + } + ) + + return targets diff --git a/scripts/rebuild_all.py b/scripts/rebuild_all.py index c89e551..9635c3a 100644 --- a/scripts/rebuild_all.py +++ b/scripts/rebuild_all.py @@ -62,6 +62,34 @@ class ExtractJob: # Manifest of everything we can rebuild. Extend as new raw years arrive on the bucket. JOBS: list[ExtractJob] = [ + ExtractJob("frs", 1994, "frs/1994", "--frs"), + ExtractJob("frs", 1995, "frs/1995", "--frs"), + ExtractJob("frs", 1996, "frs/1996", "--frs"), + ExtractJob("frs", 1997, "frs/1997", "--frs"), + ExtractJob("frs", 1998, "frs/1998", "--frs"), + ExtractJob("frs", 1999, "frs/1999", "--frs"), + ExtractJob("frs", 2000, "frs/2000", "--frs"), + ExtractJob("frs", 2001, "frs/2001", "--frs"), + ExtractJob("frs", 2002, "frs/2002", "--frs"), + ExtractJob("frs", 2003, "frs/2003", "--frs"), + ExtractJob("frs", 2004, "frs/2004", "--frs"), + ExtractJob("frs", 2005, "frs/2005", "--frs"), + ExtractJob("frs", 2006, "frs/2006", "--frs"), + ExtractJob("frs", 2007, "frs/2007", "--frs"), + ExtractJob("frs", 2008, "frs/2008", "--frs"), + ExtractJob("frs", 2009, "frs/2009", "--frs"), + ExtractJob("frs", 2010, "frs/2010", "--frs"), + ExtractJob("frs", 2011, "frs/2011", "--frs"), + ExtractJob("frs", 2012, "frs/2012", "--frs"), + ExtractJob("frs", 2013, "frs/2013", "--frs"), + ExtractJob("frs", 2014, "frs/2014", "--frs"), + ExtractJob("frs", 2015, "frs/2015", "--frs"), + ExtractJob("frs", 2016, "frs/2016", "--frs"), + ExtractJob("frs", 2017, "frs/2017", "--frs"), + ExtractJob("frs", 2018, "frs/2018", "--frs"), + ExtractJob("frs", 2019, "frs/2019", "--frs"), + ExtractJob("frs", 2020, "frs/2020", "--frs"), + ExtractJob("frs", 2021, "frs/2021", "--frs"), ExtractJob("frs", 2022, "frs/2022", "--frs"), ExtractJob("frs", 2023, "frs/2023", "--frs"), ExtractJob("lcfs", 2019, "lcfs/2019", "--lcfs"), diff --git a/src/data/calibrate.rs b/src/data/calibrate.rs new file mode 100644 index 0000000..8de515b --- /dev/null +++ b/src/data/calibrate.rs @@ -0,0 +1,678 @@ +//! Calibration / reweighting of household survey data to match administrative targets. +//! +//! Loads calibration targets from a JSON file, builds a matrix of household-level +//! contributions to each target, and optimises household weights using Adam in +//! log-space to minimise mean squared relative error. +//! +//! Calibration runs *after* a baseline simulation so that targets can reference +//! simulated output variables (income_tax, universal_credit, etc.) as well as +//! raw input data. + +use std::path::Path; + +use colored::Colorize; +use comfy_table::{Table, ContentArrangement, presets, Cell, Color}; +use rand::Rng; +use rayon::prelude::*; +use serde::Deserialize; + +use crate::data::Dataset; +use crate::engine::simulation::SimulationResults; + +// ── Target schema ────────────────────────────────────────────────────────── + +#[derive(Debug, Deserialize, Clone)] +pub struct CalibrationTargetFile { + pub targets: Vec, +} + +#[derive(Debug, Deserialize, Clone)] +#[allow(dead_code)] +pub struct CalibrationTarget { + pub name: String, + pub variable: String, + pub entity: String, + pub aggregation: String, + #[serde(default)] + pub filter: Option, + /// Benunit-level property filter (e.g. is_couple, has_children). + #[serde(default)] + pub benunit_filter: Option, + pub value: f64, + pub source: String, + pub year: u32, + #[serde(default)] + pub holdout: bool, +} + +#[derive(Debug, Deserialize, Clone)] +pub struct TargetFilter { + pub variable: String, + pub min: f64, + pub max: f64, +} + +/// Filter on benunit-level computed properties (checked via entity methods). +/// All specified conditions must be true (AND logic). +#[derive(Debug, Deserialize, Clone)] +pub struct BenunitFilter { + /// true = couple, false = single + #[serde(default)] + pub is_couple: Option, + /// true = has children, false = no children + #[serde(default)] + pub has_children: Option, + /// true = at least one person in benunit is a carer + #[serde(default)] + pub has_carer: Option, + /// true = at least one person has esa_group == 1 (support/LCWRA) + #[serde(default)] + pub has_lcwra: Option, + /// true = at least one person has esa_group == 2 (WRAG/LCW) + #[serde(default)] + pub has_lcw: Option, + /// true = benunit has rent > 0 (housing entitlement proxy) + #[serde(default)] + pub has_housing: Option, +} + +// ── Load targets ─────────────────────────────────────────────────────────── + +pub fn load_targets(path: &Path) -> anyhow::Result> { + let text = std::fs::read_to_string(path)?; + let file: CalibrationTargetFile = serde_json::from_str(&text)?; + Ok(file.targets) +} + +// ── Variable resolution ──────────────────────────────────────────────────── + +/// Get a person-level variable value by name, checking simulation results first. +fn person_variable( + p: &crate::engine::entities::Person, + sim: Option<&SimulationResults>, + pid: usize, + name: &str, +) -> f64 { + // Check simulation output variables first + if let Some(results) = sim { + if let Some(v) = person_result_variable(&results.person_results[pid], name) { + return v; + } + } + // Fall back to input data + match name { + "age" => p.age, + "employment_income" => p.employment_income, + "self_employment_income" => p.self_employment_income, + "pension_income" | "private_pension_income" => p.pension_income, + "state_pension" => p.state_pension, + "savings_interest_income" | "savings_interest" => p.savings_interest_income, + "dividend_income" => p.dividend_income, + "capital_gains" => p.capital_gains, + "property_income" => p.property_income, + "maintenance_income" => p.maintenance_income, + "miscellaneous_income" => p.miscellaneous_income, + "other_income" => p.other_income, + "child_benefit" => p.child_benefit, + "housing_benefit" => p.housing_benefit, + "income_support" => p.income_support, + "pension_credit" => p.pension_credit, + "child_tax_credit" => p.child_tax_credit, + "working_tax_credit" => p.working_tax_credit, + "universal_credit" => p.universal_credit, + "dla_care" => p.dla_care, + "dla_mobility" => p.dla_mobility, + "pip_daily_living" => p.pip_daily_living, + "pip_mobility" => p.pip_mobility, + "carers_allowance" => p.carers_allowance, + "attendance_allowance" => p.attendance_allowance, + "esa_income" => p.esa_income, + "esa_contributory" => p.esa_contributory, + "jsa_income" => p.jsa_income, + "jsa_contributory" => p.jsa_contributory, + "other_benefits" => p.other_benefits, + "total_income" => p.total_income(), + "hours_worked" => p.hours_worked, + _ => 0.0, + } +} + +/// Get a simulation output variable for a person. Returns None if not a sim variable. +fn person_result_variable( + pr: &crate::engine::simulation::PersonResult, + name: &str, +) -> Option { + match name { + "income_tax" => Some(pr.income_tax), + "national_insurance" | "employee_ni" => Some(pr.national_insurance), + "employer_ni" => Some(pr.employer_ni), + "total_ni" => Some(pr.national_insurance + pr.employer_ni), + "sim_total_income" => Some(pr.total_income), + "taxable_income" => Some(pr.taxable_income), + "personal_allowance" => Some(pr.personal_allowance), + "adjusted_net_income" => Some(pr.adjusted_net_income), + "hicbc" => Some(pr.hicbc), + "capital_gains_tax" => Some(pr.capital_gains_tax), + _ => None, + } +} + +/// Get a simulation output variable for a benefit unit. +fn benunit_result_variable( + br: &crate::engine::simulation::BenUnitResult, + name: &str, +) -> Option { + match name { + "universal_credit" => Some(br.universal_credit), + "child_benefit" => Some(br.child_benefit), + "state_pension" => Some(br.state_pension), + "pension_credit" => Some(br.pension_credit), + "housing_benefit" => Some(br.housing_benefit), + "child_tax_credit" => Some(br.child_tax_credit), + "working_tax_credit" => Some(br.working_tax_credit), + "income_support" => Some(br.income_support), + "esa_income_related" => Some(br.esa_income_related), + "jsa_income_based" => Some(br.jsa_income_based), + "carers_allowance" => Some(br.carers_allowance), + "total_benefits" => Some(br.total_benefits), + "uc_max_amount" => Some(br.uc_max_amount), + "uc_income_reduction" => Some(br.uc_income_reduction), + "benefit_cap_reduction" => Some(br.benefit_cap_reduction), + _ => None, + } +} + +/// Get a household-level variable value by name. +fn household_variable( + h: &crate::engine::entities::Household, + sim: Option<&SimulationResults>, + hh_idx: usize, + name: &str, +) -> f64 { + // Check simulation output variables first + if let Some(results) = sim { + if let Some(v) = household_result_variable(&results.household_results[hh_idx], name) { + return v; + } + } + match name { + "council_tax_annual" | "council_tax" => h.council_tax, + "rent_annual" | "rent" => h.rent, + "weight" => h.weight, + "household_id" => 1.0, + "property_wealth" => h.property_wealth, + "net_financial_wealth" => h.net_financial_wealth, + "gross_financial_wealth" => h.gross_financial_wealth, + "savings" => h.savings, + _ => 0.0, + } +} + +/// Get a simulation output variable for a household. +fn household_result_variable( + hr: &crate::engine::simulation::HouseholdResult, + name: &str, +) -> Option { + match name { + "net_income" => Some(hr.net_income), + "total_tax" => Some(hr.total_tax), + "hh_total_benefits" => Some(hr.total_benefits), + "gross_income" => Some(hr.gross_income), + "vat" => Some(hr.vat), + "fuel_duty" => Some(hr.fuel_duty), + "capital_gains_tax" => Some(hr.capital_gains_tax), + "stamp_duty" => Some(hr.stamp_duty), + "council_tax_calculated" => Some(hr.council_tax_calculated), + _ => None, + } +} + +/// Check whether a benunit passes all conditions in a BenunitFilter. +fn benunit_passes_filter( + bu: &crate::engine::entities::BenUnit, + people: &[crate::engine::entities::Person], + filter: &BenunitFilter, +) -> bool { + if let Some(want_couple) = filter.is_couple { + if bu.is_couple(people) != want_couple { + return false; + } + } + if let Some(want_children) = filter.has_children { + let has = bu.num_children(people) > 0; + if has != want_children { + return false; + } + } + if let Some(want_carer) = filter.has_carer { + let has = bu.person_ids.iter().any(|&pid| people[pid].is_carer); + if has != want_carer { + return false; + } + } + if let Some(want_lcwra) = filter.has_lcwra { + let has = bu.person_ids.iter().any(|&pid| people[pid].esa_group == 1); + if has != want_lcwra { + return false; + } + } + if let Some(want_lcw) = filter.has_lcw { + let has = bu.person_ids.iter().any(|&pid| people[pid].esa_group == 2); + if has != want_lcw { + return false; + } + } + if let Some(want_housing) = filter.has_housing { + let has = bu.rent_monthly > 0.0; + if has != want_housing { + return false; + } + } + true +} + +// ── Matrix building ──────────────────────────────────────────────────────── + +/// Build the calibration matrix M[i][j] and target vector y[j]. +/// +/// M[i][j] = household i's contribution to target j (before weighting). +/// y[j] = the target value. +/// +/// If `sim_results` is provided, simulation output variables can be used +/// in addition to raw input data. +/// +/// Returns (matrix, target_values, training_mask) where training_mask[j] +/// is true if target j should be included in the loss. +pub fn build_matrix( + dataset: &Dataset, + targets: &[CalibrationTarget], + sim_results: Option<&SimulationResults>, +) -> (Vec>, Vec, Vec) { + let n_hh = dataset.households.len(); + let n_targets = targets.len(); + let mut matrix = vec![vec![0.0f64; n_targets]; n_hh]; + let mut target_values = vec![0.0f64; n_targets]; + let mut training_mask = vec![true; n_targets]; + + for (j, target) in targets.iter().enumerate() { + target_values[j] = target.value; + training_mask[j] = !target.holdout; + + match target.entity.as_str() { + "person" => { + for (i, hh) in dataset.households.iter().enumerate() { + let mut contribution = 0.0f64; + for &pid in &hh.person_ids { + let person = &dataset.people[pid]; + + if let Some(ref filter) = target.filter { + let filter_val = person_variable(person, sim_results, pid, &filter.variable); + if filter_val < filter.min || filter_val >= filter.max { + continue; + } + } + + match target.aggregation.as_str() { + "sum" => { + contribution += person_variable(person, sim_results, pid, &target.variable); + } + "count_nonzero" => { + if person_variable(person, sim_results, pid, &target.variable) > 0.0 { + contribution += 1.0; + } + } + "count" => { + contribution += 1.0; + } + _ => {} + } + } + matrix[i][j] = contribution; + } + } + "benunit" => { + for (i, hh) in dataset.households.iter().enumerate() { + let mut contribution = 0.0f64; + for &bu_id in &hh.benunit_ids { + let bu = &dataset.benunits[bu_id]; + + // Apply benunit-level property filter if present + if let Some(ref bf) = target.benunit_filter { + if !benunit_passes_filter(bu, &dataset.people, bf) { + continue; + } + } + + // For benunit variables, check simulation results first + let bu_val = if let Some(results) = sim_results { + benunit_result_variable(&results.benunit_results[bu_id], &target.variable) + .unwrap_or(0.0) + } else { + // Fall back to input data: sum person-level variable across benunit members + bu.person_ids.iter() + .map(|&pid| person_variable(&dataset.people[pid], None, pid, &target.variable)) + .sum::() + }; + + match target.aggregation.as_str() { + "sum" => { + contribution += bu_val; + } + "count_nonzero" => { + if bu_val > 0.0 { + contribution += 1.0; + } + } + "count" => { + contribution += 1.0; + } + _ => {} + } + } + matrix[i][j] = contribution; + } + } + "household" => { + for (i, hh) in dataset.households.iter().enumerate() { + match target.aggregation.as_str() { + "sum" => { + matrix[i][j] = household_variable(hh, sim_results, i, &target.variable); + } + "count" | "count_nonzero" => { + let val = household_variable(hh, sim_results, i, &target.variable); + matrix[i][j] = if val > 0.0 { 1.0 } else { 0.0 }; + } + _ => {} + } + } + } + _ => {} + } + } + + // Skip targets where no household contributes (matrix column all zero). + let mut n_skipped = 0; + for j in 0..n_targets { + let col_sum: f64 = (0..n_hh).map(|i| matrix[i][j].abs()).sum(); + if col_sum < 1e-10 { + training_mask[j] = false; + n_skipped += 1; + } + } + if n_skipped > 0 { + eprintln!(" Skipped {} targets with no survey representation", n_skipped); + } + + (matrix, target_values, training_mask) +} + +// ── Adam optimiser ───────────────────────────────────────────────────────── + +/// Calibration configuration. +pub struct CalibrateConfig { + pub epochs: usize, + pub lr: f64, + pub beta1: f64, + pub beta2: f64, + pub eps: f64, + pub dropout: f64, + pub log_interval: usize, +} + +impl Default for CalibrateConfig { + fn default() -> Self { + CalibrateConfig { + epochs: 512, + lr: 0.1, + beta1: 0.9, + beta2: 0.999, + eps: 1e-8, + dropout: 0.05, + log_interval: 50, + } + } +} + +/// Result of calibration. +pub struct CalibrateResult { + pub weights: Vec, + pub final_training_loss: f64, + pub final_holdout_loss: f64, + pub per_target_error: Vec<(String, f64, f64, f64, bool)>, +} + +/// Run Adam optimisation to find weights minimising MSRE against targets. +pub fn calibrate( + matrix: &[Vec], + target_values: &[f64], + training_mask: &[bool], + initial_weights: &[f64], + config: &CalibrateConfig, +) -> CalibrateResult { + let n_hh = matrix.len(); + let n_targets = target_values.len(); + let n_training = training_mask.iter().filter(|&&m| m).count(); + + if n_hh == 0 || n_targets == 0 || n_training == 0 { + return CalibrateResult { + weights: initial_weights.to_vec(), + final_training_loss: 0.0, + final_holdout_loss: 0.0, + per_target_error: Vec::new(), + }; + } + + let mut u: Vec = initial_weights.iter() + .map(|&w| if w > 0.0 { w.ln() } else { 0.0 }) + .collect(); + + let mut m = vec![0.0f64; n_hh]; + let mut v = vec![0.0f64; n_hh]; + + let mut rng = rand::thread_rng(); + + for epoch in 0..config.epochs { + let weights: Vec = u.iter().enumerate().map(|(_i, &ui)| { + let w = ui.exp(); + if config.dropout > 0.0 && rng.gen::() < config.dropout { + 0.0 + } else { + w / (1.0 - config.dropout) + } + }).collect(); + + let predictions: Vec = (0..n_targets).into_par_iter().map(|j| { + let mut pred = 0.0f64; + for i in 0..n_hh { + pred += weights[i] * matrix[i][j]; + } + pred + }).collect(); + + let residuals: Vec = (0..n_targets).map(|j| { + if target_values[j].abs() > 1.0 { + predictions[j] / target_values[j] - 1.0 + } else { + 0.0 + } + }).collect(); + + let training_loss: f64 = residuals.iter().enumerate() + .filter(|(j, _)| training_mask[*j]) + .map(|(_, r)| r * r) + .sum::() / n_training as f64; + + let n_holdout = training_mask.iter().filter(|&&m| !m).count(); + let holdout_loss = if n_holdout > 0 { + residuals.iter().enumerate() + .filter(|(j, _)| !training_mask[*j]) + .map(|(_, r)| r * r) + .sum::() / n_holdout as f64 + } else { + 0.0 + }; + + if epoch % config.log_interval == 0 || epoch == config.epochs - 1 { + eprintln!( + " Epoch {:>4}/{}: training RMSRE {:.2}%, holdout RMSRE {:.2}%", + epoch, config.epochs, + training_loss.sqrt() * 100.0, + holdout_loss.sqrt() * 100.0, + ); + } + + if epoch == config.epochs - 1 { + let final_weights: Vec = u.iter().map(|&ui| ui.exp()).collect(); + let final_preds: Vec = (0..n_targets).map(|j| { + (0..n_hh).map(|i| final_weights[i] * matrix[i][j]).sum() + }).collect(); + + let final_training_loss: f64 = (0..n_targets) + .filter(|&j| training_mask[j]) + .map(|j| { + let r = if target_values[j].abs() > 1.0 { + final_preds[j] / target_values[j] - 1.0 + } else { 0.0 }; + r * r + }).sum::() / n_training as f64; + + let final_holdout_loss = if n_holdout > 0 { + (0..n_targets) + .filter(|&j| !training_mask[j]) + .map(|j| { + let r = if target_values[j].abs() > 1.0 { + final_preds[j] / target_values[j] - 1.0 + } else { 0.0 }; + r * r + }).sum::() / n_holdout as f64 + } else { 0.0 }; + + return CalibrateResult { + weights: final_weights, + final_training_loss, + final_holdout_loss, + per_target_error: (0..n_targets).map(|j| { + let rel_err = if target_values[j].abs() > 1.0 { + final_preds[j] / target_values[j] - 1.0 + } else { 0.0 }; + (String::new(), final_preds[j], target_values[j], rel_err, !training_mask[j]) + }).collect(), + }; + } + + let grad: Vec = (0..n_hh).into_par_iter().map(|i| { + let w_i = weights[i]; + let mut g = 0.0f64; + for j in 0..n_targets { + if training_mask[j] && target_values[j].abs() > 1.0 { + g += residuals[j] * matrix[i][j] * w_i / target_values[j]; + } + } + 2.0 * g / n_training as f64 + }).collect(); + + let t = (epoch + 1) as f64; + let bc1 = 1.0 - config.beta1.powf(t); + let bc2 = 1.0 - config.beta2.powf(t); + + for i in 0..n_hh { + m[i] = config.beta1 * m[i] + (1.0 - config.beta1) * grad[i]; + v[i] = config.beta2 * v[i] + (1.0 - config.beta2) * grad[i] * grad[i]; + let m_hat = m[i] / bc1; + let v_hat = v[i] / bc2; + u[i] -= config.lr * m_hat / (v_hat.sqrt() + config.eps); + } + } + + let final_weights: Vec = u.iter().map(|&ui| ui.exp()).collect(); + CalibrateResult { + weights: final_weights, + final_training_loss: 0.0, + final_holdout_loss: 0.0, + per_target_error: Vec::new(), + } +} + +// ── Reporting ────────────────────────────────────────────────────────────── + +pub fn print_report( + targets: &[CalibrationTarget], + result: &CalibrateResult, + dataset: &Dataset, +) { + let total_weight: f64 = result.weights.iter().sum(); + let original_weight: f64 = dataset.households.iter().map(|h| h.weight).sum(); + + eprintln!("\n{}", "Calibration complete".bright_green().bold()); + eprintln!( + " Households: {} Original weight sum: {:.0} Calibrated weight sum: {:.0}", + dataset.households.len(), original_weight, total_weight + ); + eprintln!( + " Training RMSRE: {:.2}% Holdout RMSRE: {:.2}%", + result.final_training_loss.sqrt() * 100.0, + result.final_holdout_loss.sqrt() * 100.0, + ); + + let mut rows: Vec<(usize, &str, f64, f64, f64, bool)> = result.per_target_error.iter().enumerate() + .map(|(j, (_, pred, target, rel_err, holdout))| { + (j, targets[j].name.as_str(), *pred, *target, *rel_err, *holdout) + }) + .collect(); + + rows.sort_by(|a, b| b.4.abs().partial_cmp(&a.4.abs()).unwrap_or(std::cmp::Ordering::Equal)); + + let mut table = Table::new(); + table.load_preset(presets::UTF8_FULL_CONDENSED); + table.set_content_arrangement(ContentArrangement::Dynamic); + table.set_header(vec!["Target", "Predicted", "Actual", "Rel error", "Type"]); + + let show_count = 30; + for (idx, (_, name, pred, target, rel_err, holdout)) in rows.iter().enumerate() { + if idx >= show_count && !holdout { + continue; + } + let err_pct = rel_err * 100.0; + let err_color = if err_pct.abs() < 5.0 { + Color::Green + } else if err_pct.abs() < 15.0 { + Color::Yellow + } else { + Color::Red + }; + let type_str = if *holdout { "holdout" } else { "training" }; + + table.add_row(vec![ + Cell::new(name), + Cell::new(format_value(*pred)), + Cell::new(format_value(*target)), + Cell::new(format!("{:+.1}%", err_pct)).fg(err_color), + Cell::new(type_str), + ]); + } + + eprintln!("\n{table}"); +} + +fn format_value(v: f64) -> String { + let abs = v.abs(); + if abs >= 1e9 { + format!("£{:.1}bn", v / 1e9) + } else if abs >= 1e6 { + format!("£{:.1}m", v / 1e6) + } else if abs >= 1e3 { + format!("{:.0}k", v / 1e3) + } else { + format!("{:.0}", v) + } +} + +// ── Apply weights ────────────────────────────────────────────────────────── + +pub fn apply_weights(dataset: &mut Dataset, weights: &[f64]) { + for (i, hh) in dataset.households.iter_mut().enumerate() { + if i < weights.len() { + hh.weight = weights[i]; + } + } +} diff --git a/src/data/mod.rs b/src/data/mod.rs index 0fdc9c2..f734710 100644 --- a/src/data/mod.rs +++ b/src/data/mod.rs @@ -5,6 +5,7 @@ pub mod spi; pub mod lcfs; pub mod was; pub mod efrs; +pub mod calibrate; use crate::engine::entities::*; diff --git a/src/main.rs b/src/main.rs index a3a71b8..a6920ac 100644 --- a/src/main.rs +++ b/src/main.rs @@ -20,6 +20,7 @@ use crate::data::was::load_was; use crate::data::clean::{write_clean_csvs, load_clean_dataset, write_microdata, write_microdata_to_stdout}; use crate::data::stdin::load_dataset_from_reader; use crate::data::efrs; +use crate::data::calibrate; #[derive(Parser)] #[command(name = "policyengine-uk")] @@ -123,6 +124,20 @@ struct Cli { #[arg(long)] lcfs_dir: Option, + // ── Calibration ── + + /// Calibration targets JSON file. Runs reweighting before any simulation. + #[arg(long)] + calibrate: Option, + + /// Output directory for calibrated dataset CSVs. + #[arg(long)] + calibrate_output: Option, + + /// Number of calibration epochs (default: 512). + #[arg(long, default_value = "512")] + calibrate_epochs: usize, + // ── Parameter inspection ── /// Export baseline parameters as JSON. @@ -405,6 +420,76 @@ fn main() -> anyhow::Result<()> { return Ok(()); } + // Calibrate dataset weights if requested + if let Some(targets_path) = &cli.calibrate { + let base = cli.data.as_ref() + .ok_or_else(|| anyhow::anyhow!("--calibrate requires --data "))?; + let year_dir = base.join(cli.year.to_string()); + let mut dataset = if year_dir.is_dir() { + eprintln!("Loading clean data {}/{}...", cli.year, (cli.year + 1) % 100); + load_clean_dataset(&year_dir, cli.year)? + } else { + let latest = (1994..=cli.year).rev() + .find(|y| base.join(y.to_string()).is_dir()) + .ok_or_else(|| anyhow::anyhow!("No clean data found in {}", base.display()))?; + eprintln!("Loading clean data {}/{} and uprating to {}/{}...", + latest, (latest + 1) % 100, cli.year, (cli.year + 1) % 100); + let mut ds = load_clean_dataset(&base.join(latest.to_string()), latest)?; + ds.uprate_to(cli.year); + ds + }; + eprintln!("Loaded {} households, {} people", dataset.households.len(), dataset.people.len()); + + // Load and filter targets for the requested year + let all_targets = calibrate::load_targets(targets_path)?; + let targets: Vec<_> = all_targets.into_iter() + .filter(|t| t.year == cli.year) + .collect(); + eprintln!("Loaded {} calibration targets for {}", targets.len(), cli.year); + + if targets.is_empty() { + anyhow::bail!("No calibration targets found for year {}", cli.year); + } + + // Run baseline simulation so calibration can use output variables + let params = Parameters::for_year(cli.year) + .unwrap_or_else(|e| panic!("Failed to load params {}/{}: {}", cli.year, cli.year + 1, e)); + eprintln!("Running baseline simulation..."); + let sim = Simulation::new( + dataset.people.clone(), dataset.benunits.clone(), + dataset.households.clone(), params, cli.year, + ); + let sim_results = sim.run(); + + // Build calibration matrix using simulation outputs + let (matrix, target_values, training_mask) = calibrate::build_matrix(&dataset, &targets, Some(&sim_results)); + let initial_weights: Vec = dataset.households.iter().map(|h| h.weight).collect(); + + // Run optimisation + eprintln!("Running calibration ({} epochs)...", cli.calibrate_epochs); + let config = calibrate::CalibrateConfig { + epochs: cli.calibrate_epochs, + ..Default::default() + }; + let mut result = calibrate::calibrate(&matrix, &target_values, &training_mask, &initial_weights, &config); + + // Fill in target names for reporting + for (j, entry) in result.per_target_error.iter_mut().enumerate() { + entry.0 = targets[j].name.clone(); + } + + calibrate::print_report(&targets, &result, &dataset); + + // Write calibrated dataset + calibrate::apply_weights(&mut dataset, &result.weights); + let output_dir = cli.calibrate_output.as_ref() + .unwrap_or(&year_dir); + std::fs::create_dir_all(output_dir)?; + write_clean_csvs(&mut dataset, output_dir)?; + eprintln!("Wrote calibrated dataset to {}", output_dir.display()); + return Ok(()); + } + // Load dataset for simulation let dataset = if cli.stdin_data { load_dataset_from_reader(std::io::BufReader::new(std::io::stdin().lock()), cli.year)? diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..fa3afb4 --- /dev/null +++ b/uv.lock @@ -0,0 +1,751 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'", + 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