Address PR #505 review feedback

Makefile

@@ -1,4 +1,6 @@
-.PHONY: all format test install download upload docker documentation data publish-local-area clean build paper clean-paper presentations database database-refresh promote-database
+.PHONY: all format test install download upload docker documentation data publish-local-area clean build paper clean-paper presentations database database-refresh promote-database promote-dataset
+
+HF_CLONE_DIR = $(HOME)/devl/huggingface/policyengine-us-data
 
 all: data test
 
@@ -72,12 +74,17 @@ database-refresh:
 
 promote-database:
 	cp policyengine_us_data/storage/calibration/policy_data.db \
-		$(HOME)/devl/huggingface/policyengine-us-data/calibration/policy_data.db
-	rm -rf $(HOME)/devl/huggingface/policyengine-us-data/calibration/raw_inputs
+		$(HF_CLONE_DIR)/calibration/policy_data.db
+	rm -rf $(HF_CLONE_DIR)/calibration/raw_inputs
 	cp -r policyengine_us_data/storage/calibration/raw_inputs \
-		$(HOME)/devl/huggingface/policyengine-us-data/calibration/raw_inputs
+		$(HF_CLONE_DIR)/calibration/raw_inputs
 	@echo "Copied DB and raw_inputs to HF clone. Now cd to HF repo, commit, and push."
 
+promote-dataset:
+	cp policyengine_us_data/storage/stratified_extended_cps_2024.h5 \
+		$(HF_CLONE_DIR)/calibration/stratified_extended_cps.h5
+	@echo "Copied dataset to HF clone. Now cd to HF repo, commit, and push."
+
 data: download
 	python policyengine_us_data/utils/uprating.py
 	python policyengine_us_data/datasets/acs/acs.py
@@ -87,6 +94,10 @@ data: download
 	python policyengine_us_data/datasets/cps/extended_cps.py
 	python policyengine_us_data/datasets/cps/enhanced_cps.py
 	python policyengine_us_data/datasets/cps/small_enhanced_cps.py
+	# 12000: number of households our GPUs can handle (found via trial and error).
+	# --top=99.5: include only top 0.5% (vs default 1%) to preserve
+	#   representation of lower-income households.
+	# --seed=3526: reproducible stratified sampling.
 	python policyengine_us_data/datasets/cps/local_area_calibration/create_stratified_cps.py 12000 --top=99.5 --seed=3526
 
 publish-local-area:

changelog_entry.yaml

@@ -0,0 +1,10 @@
+- bump: patch
+  changes:
+    changed:
+    - Switch DEFAULT_DATASET to local storage path for database ETL scripts
+    - Extract shared etl_argparser() to reduce boilerplate across 7 ETL scripts
+    - Delete dead get_pseudo_input_variables() function
+    added:
+    - promote-dataset Makefile target
+    - Year-mismatch warning in national targets ETL
+    - Congress-session constants and warning in SOI district puller

policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py

@@ -248,30 +248,6 @@ def get_calculated_variables(sim) -> List[str]:
     return result
 
 
-def get_pseudo_input_variables(sim) -> set:
-    """
-    Identify pseudo-input variables that should NOT be saved to H5 files.
-
-    NOTE: This function currently returns an empty set. The original logic
-    excluded variables with 'adds' or 'subtracts' attributes, but analysis
-    showed that in CPS data, these variables contain authoritative stored
-    data that does NOT match their component variables:
-
-    - pre_tax_contributions: components are all 0, aggregate has imputed values
-    - tax_exempt_pension_income: aggregate has 135M, components only 20M
-    - taxable_pension_income: aggregate has 82M, components only 29M
-    - interest_deduction: aggregate has 41M, components are 0
-
-    The 'adds' attribute defines how to CALCULATE these values, but in CPS
-    data the stored values are the authoritative source. Excluding them and
-    recalculating from components produces incorrect results.
-
-    For geo-stacking, entity ID reindexing preserves within-entity
-    relationships, so aggregation within a person or tax_unit remains valid.
-    """
-    return set()
-
-
 def apply_op(values: np.ndarray, op: str, val: str) -> np.ndarray:
     """Apply constraint operation to values array."""
     try:

policyengine_us_data/datasets/cps/local_area_calibration/create_stratified_cps.py

@@ -13,9 +13,6 @@
 from policyengine_us import Microsimulation
 from policyengine_core.data.dataset import Dataset
 from policyengine_core.enums import Enum
-from policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import (
-    get_pseudo_input_variables,
-)
 
 
 def create_stratified_cps_dataset(
@@ -225,16 +222,6 @@ def create_stratified_cps_dataset(
 
     # Only save input variables (not calculated/derived variables)
     input_vars = set(sim.input_variables)
-
-    # Filter out pseudo-inputs: variables with adds/subtracts that aggregate
-    # formula-based components. These have stale values that corrupt calculations.
-    pseudo_inputs = get_pseudo_input_variables(sim)
-    if pseudo_inputs:
-        print(f"Excluding {len(pseudo_inputs)} pseudo-input variables:")
-        for var in sorted(pseudo_inputs):
-            print(f"  - {var}")
-        input_vars = input_vars - pseudo_inputs
-
     print(f"Found {len(input_vars)} input variables to save")
 
     for variable in stratified_sim.tax_benefit_system.variables:

policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py

@@ -14,7 +14,6 @@
 from policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import (
     get_all_cds_from_database,
     get_calculated_variables,
-    get_pseudo_input_variables,
     STATE_CODES,
     STATE_FIPS_TO_NAME,
     STATE_FIPS_TO_CODE,
@@ -624,17 +623,7 @@ def create_sparse_cd_stacked_dataset(
 
     # Only save input variables (not calculated/derived variables)
     # Calculated variables like state_name, state_code will be recalculated on load
-    input_vars = set(base_sim.input_variables)
-
-    # Filter out pseudo-inputs: variables with adds/subtracts that aggregate
-    # formula-based components. These have stale values that corrupt calculations.
-    pseudo_inputs = get_pseudo_input_variables(base_sim)
-    if pseudo_inputs:
-        print(f"Excluding {len(pseudo_inputs)} pseudo-input variables:")
-        for var in sorted(pseudo_inputs):
-            print(f"  - {var}")
-
-    vars_to_save = input_vars - pseudo_inputs
+    vars_to_save = set(base_sim.input_variables)
     print(f"Found {len(vars_to_save)} input variables to save")
 
     # congressional_district_geoid isn't in the original microdata and has no formula,

policyengine_us_data/db/create_initial_strata.py

@@ -1,4 +1,3 @@
-import argparse
 import logging
 from typing import Dict
 
@@ -7,12 +6,11 @@
 from sqlmodel import Session, create_engine
 
 from policyengine_us_data.storage import STORAGE_FOLDER
-
-DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
 )
+from policyengine_us_data.utils.db import etl_argparser
 from policyengine_us_data.utils.raw_cache import (
     is_cached,
     save_json,
@@ -71,27 +69,7 @@ def fetch_congressional_districts(year):
 
 
 def main():
-    parser = argparse.ArgumentParser(
-        description="Create initial geographic strata for calibration"
-    )
-    parser.add_argument(
-        "--dataset",
-        default=DEFAULT_DATASET,
-        help=(
-            "Source dataset (local path or HuggingFace URL). "
-            "The year for Census API calls is derived from the dataset's "
-            "default_calculation_period. Default: %(default)s"
-        ),
-    )
-    args = parser.parse_args()
-
-    # Derive year from dataset
-    from policyengine_us import Microsimulation
-
-    print(f"Loading dataset: {args.dataset}")
-    sim = Microsimulation(dataset=args.dataset)
-    year = int(sim.default_calculation_period)
-    print(f"Derived year from dataset: {year}")
+    _, year = etl_argparser("Create initial geographic strata for calibration")
 
     # State FIPS to name/abbreviation mapping
     STATE_NAMES = {

policyengine_us_data/db/etl_age.py

@@ -1,21 +1,20 @@
-import argparse
-
 import pandas as pd
 import numpy as np
 from sqlmodel import Session, create_engine, select
 
 from policyengine_us_data.storage import STORAGE_FOLDER
-
-DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
-
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
     Target,
     SourceType,
 )
 from policyengine_us_data.utils.census import get_census_docs, pull_acs_table
-from policyengine_us_data.utils.db import parse_ucgid, get_geographic_strata
+from policyengine_us_data.utils.db import (
+    parse_ucgid,
+    get_geographic_strata,
+    etl_argparser,
+)
 from policyengine_us_data.utils.db_metadata import (
     get_or_create_source,
     get_or_create_variable_group,
@@ -287,27 +286,7 @@ def load_age_data(df_long, geo, year):
 
 
 def main():
-    parser = argparse.ArgumentParser(
-        description="ETL for age calibration targets"
-    )
-    parser.add_argument(
-        "--dataset",
-        default=DEFAULT_DATASET,
-        help=(
-            "Source dataset (local path or HuggingFace URL). "
-            "The year for Census API calls is derived from the dataset's "
-            "default_calculation_period. Default: %(default)s"
-        ),
-    )
-    args = parser.parse_args()
-
-    # Derive year from dataset
-    from policyengine_us import Microsimulation
-
-    print(f"Loading dataset: {args.dataset}")
-    sim = Microsimulation(dataset=args.dataset)
-    year = int(sim.default_calculation_period)
-    print(f"Derived year from dataset: {year}")
+    _, year = etl_argparser("ETL for age calibration targets")
 
     # --- ETL: Extract, Transform, Load ----
 

policyengine_us_data/db/etl_irs_soi.py

@@ -1,4 +1,3 @@
-import argparse
 import logging
 from typing import Optional
 
@@ -8,19 +7,6 @@
 from sqlmodel import Session, create_engine, select
 
 from policyengine_us_data.storage import STORAGE_FOLDER
-
-DEFAULT_DATASET = "hf://policyengine/policyengine-us-data/calibration/stratified_extended_cps.h5"
-
-# IRS SOI data is typically available ~2 years after the tax year
-IRS_SOI_LAG_YEARS = 2
-from policyengine_us_data.utils.raw_cache import (
-    is_cached,
-    cache_path,
-    save_bytes,
-)
-
-logger = logging.getLogger(__name__)
-
 from policyengine_us_data.db.create_database_tables import (
     Stratum,
     StratumConstraint,
@@ -34,6 +20,7 @@
     get_stratum_parent,
     parse_ucgid,
     get_geographic_strata,
+    etl_argparser,
 )
 from policyengine_us_data.utils.db_metadata import (
     get_or_create_source,
@@ -44,6 +31,17 @@
 from policyengine_us_data.storage.calibration_targets.make_district_mapping import (
     get_district_mapping,
 )
+from policyengine_us_data.utils.raw_cache import (
+    is_cached,
+    cache_path,
+    save_bytes,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# IRS SOI data is typically available ~2 years after the tax year
+IRS_SOI_LAG_YEARS = 2
 
 """See the 22incddocguide.docx manual from the IRS SOI"""
 # Language in the doc: '$10,000 under $25,000' means >= $10,000 and < $25,000
@@ -1236,40 +1234,23 @@ def load_soi_data(long_dfs, year):
 
 
 def main():
-    parser = argparse.ArgumentParser(
-        description="ETL for IRS SOI calibration targets"
-    )
-    parser.add_argument(
-        "--dataset",
-        default=DEFAULT_DATASET,
-        help=(
-            "Source dataset (local path or HuggingFace URL). "
-            "The year for IRS SOI data is derived from the dataset's "
-            "default_calculation_period minus IRS_SOI_LAG_YEARS. "
-            "Default: %(default)s"
-        ),
-    )
-    parser.add_argument(
-        "--lag",
-        type=int,
-        default=IRS_SOI_LAG_YEARS,
-        help=(
-            "Years to subtract from dataset year for IRS SOI data "
-            "(default: %(default)s, since IRS data is ~2 years behind)"
-        ),
-    )
-    args = parser.parse_args()
-
-    # Derive year from dataset with lag applied
-    from policyengine_us import Microsimulation
+    def add_lag_arg(parser):
+        parser.add_argument(
+            "--lag",
+            type=int,
+            default=IRS_SOI_LAG_YEARS,
+            help=(
+                "Years to subtract from dataset year for IRS SOI data "
+                "(default: %(default)s, since IRS data is ~2 years behind)"
+            ),
+        )
 
-    print(f"Loading dataset: {args.dataset}")
-    sim = Microsimulation(dataset=args.dataset)
-    dataset_year = int(sim.default_calculation_period)
-    year = dataset_year - args.lag
-    print(
-        f"Dataset year: {dataset_year}, IRS SOI year: {year} (lag={args.lag})"
+    args, dataset_year = etl_argparser(
+        "ETL for IRS SOI calibration targets",
+        extra_args_fn=add_lag_arg,
     )
+    year = dataset_year - args.lag
+    print(f"IRS SOI year: {year} (lag={args.lag})")
 
     # Extract -----------------------
     raw_df = extract_soi_data()