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baogorekclaudeMaxGhenis
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Make the Calibration Database first class (#488)
* Add full database schema, national targets ETL, and metadata utilities Migrate critical database infrastructure from junkyard repo: - Expand create_database_tables.py with Source, VariableGroup, and VariableMetadata tables, ConstraintOperation enum, and improved definition hash that includes parent_stratum_id - Add etl_national_targets.py for loading ~40 national calibration targets from CBO, Treasury/JCT, CMS, and other federal sources - Add utils/db_metadata.py with get_or_create helpers for sources, variable groups, and variable metadata - Add DATABASE_GUIDE.md documenting schema, stratum groups, ETL patterns, and SQL query examples - Standardize all ETL scripts to use calibration/policy_data.db path - Update Makefile database target to include national targets step Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> * Add parse_ucgid and get_geographic_strata to utils/db.py These functions were present in the junkyard repo but missing from the SEP version. Required by ETL scripts like etl_medicaid.py. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> * Migrate data pipeline from CPS 2023 to 2024 and remove unused datasets Switch the data target to use 2024 CPS data (March 2025 ASEC) instead of 2023. Add CPS_2024_Full for full-sample generation, update ExtendedCPS_2024 and local area calibration to use it. Remove CPS_2021/2022/2023_Full, PooledCPS, Pooled_3_Year_CPS_2023, ExtendedCPS_2023, dead code, and unused exports. Update database ETL scripts for strata, IRS SOI, Medicaid, and SNAP. Trim cps.py __main__ to generate only CPS_2024_Full. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Port complete DB/ETL logic with raw_cache integration and conditional strata Replace simplified DB pipeline with full implementation: - IRS SOI: 19 conditional strata groups (100-118) with filer population layer - Variables: income_tax_before_credits, rental_income, self_employment_income, net_capital_gains, and complete AGI distribution with tax_unit_count - Medicaid: 2024 admin data (CD survey disabled pending 119th Congress remap) - All ETL extract functions now use raw_cache for offline iteration New files: validate_hierarchy.py, migrate_stratum_group_ids.py, IRS_SOI_DATA_ISSUE.md Verified: 53 target groups, 32,781 targets, X_sparse (32781, 4577564) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat: atomic parallel local area publishing with Modal Volume - Add Modal Volume staging for persistent cache - Implement parallel build workers (configurable --num-workers) - Add manifest validation with SHA256 checksums - Add retry logic with exponential backoff for HF uploads - Version files under v{version}/ paths - Update latest.json atomically after all uploads succeed - Add --skip-upload flag for build-only testing Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: update uv.lock for tenacity dependency Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: correct calibration input paths for HuggingFace download Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: format code and update changelog for parallel publishing Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * feat: add staging folder approach for atomic HuggingFace deployments - Add upload_to_staging_hf, promote_staging_to_production_hf, cleanup_staging_hf - Update atomic_upload to use staging/ folder instead of versioned paths - Add migration script for moving files from versioned to production paths - Update changelog Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * fix: add time_period to calculate() calls in sparse matrix builder The sparse_matrix_builder was calling calculate() without specifying the time_period parameter, causing it to use a default year that didn't match the year used in set_input(). This resulted in SNAP and other state-dependent variables showing identical values across all states instead of properly recalculating with state-specific rules. Also updates changelog with missing items for database improvements. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * test: skip sparse matrix builder tests not used in production These tests need rework after the time_period fix to calculate(). The sparse matrix builder is not currently used in production, so skipping these tests to unblock the PR. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * chore: remove unused versioned upload functions - Remove upload_versioned_files_to_gcs (no longer used) - Remove upload_versioned_files_to_hf (no longer used) - Remove upload_manifest_and_latest (no longer used) - Remove create_latest_pointer from manifest.py These were replaced by the staging folder approach. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Remove CPS_2025 class and extrapolation logic CPS_2025 was an extrapolated dataset from CPS_2024. This is unnecessary because PolicyEngine handles uprating at simulation time - there's no need to pre-generate datasets for future years. - Remove CPS_2025 class - Remove extrapolation logic from CPS.generate() - Remove test_cps_2025_generates test For future years, use PolicyEngine's built-in uprating by specifying the desired period when running simulations. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Haiku 4.5 <noreply@anthropic.com> Co-authored-by: Max Ghenis <mghenis@gmail.com>
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‎.github/workflows/local_area_publish.yaml‎

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repository_dispatch:
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types: [calibration-updated]
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workflow_dispatch:
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inputs:
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num_workers:
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description: 'Number of parallel workers'
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required: false
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default: '8'
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type: string
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skip_upload:
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description: 'Skip upload (build only)'
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required: false
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default: false
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type: boolean
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# Trigger strategy:
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# 1. Automatic: Code changes to local_area_calibration/ pushed to main
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# 2. repository_dispatch: Calibration workflow triggers after uploading new weights
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# 3. workflow_dispatch: Manual trigger when you update weights/data on HF yourself
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# 3. workflow_dispatch: Manual trigger with optional parameters
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jobs:
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publish-local-area:
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run: pip install modal
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- name: Run local area publishing on Modal
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run: modal run modal_app/local_area.py --branch=${{ github.head_ref || github.ref_name }}
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run: |
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NUM_WORKERS="${{ github.event.inputs.num_workers || '8' }}"
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SKIP_UPLOAD="${{ github.event.inputs.skip_upload || 'false' }}"
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BRANCH="${{ github.head_ref || github.ref_name }}"
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CMD="modal run modal_app/local_area.py --branch=${BRANCH} --num-workers=${NUM_WORKERS}"
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if [ "$SKIP_UPLOAD" = "true" ]; then
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CMD="${CMD} --skip-upload"
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fi
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echo "Running: $CMD"
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$CMD

‎.gitignore‎

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!policyengine_us_data/storage/national_and_district_rents_2023.csv
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docs/.ipynb_checkpoints/
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## Raw input cache for database pipeline
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policyengine_us_data/storage/calibration/raw_inputs/
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## Batch processing checkpoints
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completed_*.txt
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‎Makefile‎

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.PHONY: all format test install download upload docker documentation data publish-local-area clean build paper clean-paper presentations
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.PHONY: all format test install download upload docker documentation data publish-local-area clean build paper clean-paper presentations database database-refresh promote-database
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all: data test
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myst start
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database:
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rm -f policyengine_us_data/storage/calibration/policy_data.db
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python policyengine_us_data/db/create_database_tables.py
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python policyengine_us_data/db/create_initial_strata.py
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python policyengine_us_data/db/etl_national_targets.py
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python policyengine_us_data/db/etl_age.py
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python policyengine_us_data/db/etl_medicaid.py
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python policyengine_us_data/db/etl_snap.py
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python policyengine_us_data/db/etl_irs_soi.py
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python policyengine_us_data/db/validate_database.py
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database-refresh:
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rm -f policyengine_us_data/storage/calibration/policy_data.db
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rm -rf policyengine_us_data/storage/calibration/raw_inputs/
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$(MAKE) database
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promote-database:
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cp policyengine_us_data/storage/calibration/policy_data.db \
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$(HOME)/devl/huggingface/policyengine-us-data/calibration/policy_data.db
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rm -rf $(HOME)/devl/huggingface/policyengine-us-data/calibration/raw_inputs
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cp -r policyengine_us_data/storage/calibration/raw_inputs \
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$(HOME)/devl/huggingface/policyengine-us-data/calibration/raw_inputs
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@echo "Copied DB and raw_inputs to HF clone. Now cd to HF repo, commit, and push."
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data: download
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python policyengine_us_data/utils/uprating.py
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python policyengine_us_data/datasets/acs/acs.py

‎changelog_entry.yaml‎

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- bump: minor
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changes:
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changed:
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- Migrated data pipeline from CPS 2023 to CPS 2024 (March 2025 ASEC)
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- Updated ExtendedCPS_2024 to use new CPS_2024_Full (full sample)
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- Updated local area calibration to use 2024 extended CPS data
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- Updated database ETL scripts for strata, IRS SOI, Medicaid, and SNAP
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- Expanded IRS SOI ETL with detailed income brackets and filing status breakdowns
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removed:
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- Removed CPS_2021_Full, CPS_2022_Full, CPS_2023_Full classes
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- Removed PooledCPS and Pooled_3_Year_CPS_2023
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- Removed ExtendedCPS_2023
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- Removed dead train_previous_year_income_model function
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- Removed unused dataset exports from __init__.py
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added:
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- Added CPS_2024_Full class for full-sample 2024 CPS generation
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- Added raw_cache utility for Census data caching
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- Added atomic parallel local area H5 publishing with Modal Volume staging
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- Added manifest validation with SHA256 checksums
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- Added HuggingFace retry logic with exponential backoff to fix timeout errors
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- Added staging folder approach for atomic HuggingFace deployments
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- Added national targets ETL for CBO projections and tax expenditure data
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- Added database hierarchy validation script
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- Added stratum_group_id migration utilities
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- Added db_metadata utilities for source and variable group management
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- Added DATABASE_GUIDE.md with comprehensive calibration database documentation
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fixed:
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- Fixed cross-state recalculation in sparse matrix builder by adding time_period to calculate() calls

‎docs/local_area_calibration_setup.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "cell-3",
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"metadata": {},
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"outputs": [],
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"source": [
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"db_path = STORAGE_FOLDER / \"calibration\" / \"policy_data.db\"\n",
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"db_uri = f\"sqlite:///{db_path}\"\n",
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"dataset_path = str(STORAGE_FOLDER / \"stratified_extended_cps_2023.h5\")\n",
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"\n",
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"engine = create_engine(db_uri)"
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]
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"source": "db_path = STORAGE_FOLDER / \"calibration\" / \"policy_data.db\"\ndb_uri = f\"sqlite:///{db_path}\"\ndataset_path = str(STORAGE_FOLDER / \"stratified_extended_cps_2024.h5\")\n\nengine = create_engine(db_uri)"
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": null,
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"id": "cell-7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"X_sparse shape: (539, 256633)\n",
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" Rows (targets): 539\n",
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" Columns (household × CD pairs): 256633\n",
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" Non-zero entries: 67,756\n",
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" Sparsity: 99.95%\n"
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]
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}
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],
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"source": [
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"sim = Microsimulation(dataset=dataset_path)\n",
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"\n",
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"builder = SparseMatrixBuilder(\n",
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" db_uri,\n",
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" time_period=2023,\n",
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" cds_to_calibrate=test_cds,\n",
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" dataset_path=dataset_path,\n",
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")\n",
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"\n",
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"targets_df, X_sparse, household_id_mapping = builder.build_matrix(\n",
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" sim, target_filter={\"stratum_group_ids\": [4], \"variables\": [\"snap\"]}\n",
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")\n",
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"\n",
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"print(f\"X_sparse shape: {X_sparse.shape}\")\n",
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"print(f\" Rows (targets): {X_sparse.shape[0]}\")\n",
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"print(f\" Columns (household × CD pairs): {X_sparse.shape[1]}\")\n",
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"print(f\" Non-zero entries: {X_sparse.nnz:,}\")\n",
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"print(f\" Sparsity: {1 - X_sparse.nnz / (X_sparse.shape[0] * X_sparse.shape[1]):.2%}\")"
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]
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"outputs": [],
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"source": "sim = Microsimulation(dataset=dataset_path)\n\nbuilder = SparseMatrixBuilder(\n db_uri,\n time_period=2024,\n cds_to_calibrate=test_cds,\n dataset_path=dataset_path,\n)\n\ntargets_df, X_sparse, household_id_mapping = builder.build_matrix(\n sim, target_filter={\"stratum_group_ids\": [4], \"variables\": [\"snap\"]}\n)\n\nprint(f\"X_sparse shape: {X_sparse.shape}\")\nprint(f\" Rows (targets): {X_sparse.shape[0]}\")\nprint(f\" Columns (household × CD pairs): {X_sparse.shape[1]}\")\nprint(f\" Non-zero entries: {X_sparse.nnz:,}\")\nprint(f\" Sparsity: {1 - X_sparse.nnz / (X_sparse.shape[0] * X_sparse.shape[1]):.2%}\")"
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": null,
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"id": "e05aaeab-3786-4ff0-a50b-34577065d2e0",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Remember, this is a North Carolina target:\n",
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"\n",
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"target_id 9372\n",
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"stratum_id 9799\n",
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"variable snap\n",
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"value 4041086120.0\n",
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"period 2023\n",
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"stratum_group_id 4\n",
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"geographic_id 37\n",
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"Name: 80, dtype: object\n",
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"\n",
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"Household donated to NC's 2nd district, 2023 SNAP dollars:\n",
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"789.19995\n",
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"\n",
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"Household donated to NC's 2nd district, 2023 SNAP dollars:\n",
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"0.0\n"
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]
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}
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],
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"source": [
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"print(\"Remember, this is a North Carolina target:\\n\")\n",
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"print(targets_df.iloc[row_loc])\n",
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"\n",
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"print(\"\\nNC State target. Household donated to NC's 2nd district, 2023 SNAP dollars:\")\n",
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"print(X_sparse[row_loc, positions['3702']]) # Household donated to NC's 2nd district\n",
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"\n",
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"print(\"\\nSame target, same household, donated to AK's at Large district, 2023 SNAP dollars:\")\n",
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"print(X_sparse[row_loc, positions['201']]) # Household donated to AK's at Large District"
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]
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"outputs": [],
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"source": "print(\"Remember, this is a North Carolina target:\\n\")\nprint(targets_df.iloc[row_loc])\n\nprint(\"\\nNC State target. Household donated to NC's 2nd district, 2024 SNAP dollars:\")\nprint(X_sparse[row_loc, positions['3702']]) # Household donated to NC's 2nd district\n\nprint(\"\\nSame target, same household, donated to AK's at Large district, 2024 SNAP dollars:\")\nprint(X_sparse[row_loc, positions['201']]) # Household donated to AK's at Large District"
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"id": "ac59b6f1-859f-4246-8a05-8cb26384c882",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Household donated to AK's 1st district, 2023 SNAP dollars:\n",
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"342.48004\n"
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]
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}
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],
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"source": [
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"print(\"\\nHousehold donated to AK's 1st district, 2023 SNAP dollars:\")\n",
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"print(X_sparse[new_row_loc, positions['201']]) # Household donated to AK's at Large District"
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]
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"outputs": [],
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"source": "print(\"\\nHousehold donated to AK's 1st district, 2024 SNAP dollars:\")\nprint(X_sparse[new_row_loc, positions['201']]) # Household donated to AK's at Large District"
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"id": "cell-19",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SNAP values for first 5 households under different state rules:\n",
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" NC rules: [789.19995117 0. 0. 0. 0. ]\n",
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" AK rules: [342.4800415 0. 0. 0. 0. ]\n",
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" Difference: [-446.71990967 0. 0. 0. 0. ]\n"
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]
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}
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],
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"source": [
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"def create_state_simulation(state_fips):\n",
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" \"\"\"Create a simulation with all households assigned to a specific state.\"\"\"\n",
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" s = Microsimulation(dataset=dataset_path)\n",
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" s.set_input(\n",
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" \"state_fips\", 2023, np.full(hh_snap_df.shape[0], state_fips, dtype=np.int32)\n",
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" )\n",
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" for var in get_calculated_variables(s):\n",
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" s.delete_arrays(var)\n",
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" return s\n",
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"\n",
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"# Compare SNAP for first 5 households under NC vs AK rules\n",
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"nc_sim = create_state_simulation(37) # NC\n",
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"ak_sim = create_state_simulation(2) # AK\n",
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"\n",
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"nc_snap = nc_sim.calculate(\"snap\", map_to=\"household\").values[:5]\n",
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"ak_snap = ak_sim.calculate(\"snap\", map_to=\"household\").values[:5]\n",
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"\n",
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"print(\"SNAP values for first 5 households under different state rules:\")\n",
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"print(f\" NC rules: {nc_snap}\")\n",
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"print(f\" AK rules: {ak_snap}\")\n",
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"print(f\" Difference: {ak_snap - nc_snap}\")"
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]
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"outputs": [],
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"source": "def create_state_simulation(state_fips):\n \"\"\"Create a simulation with all households assigned to a specific state.\"\"\"\n s = Microsimulation(dataset=dataset_path)\n s.set_input(\n \"state_fips\", 2024, np.full(hh_snap_df.shape[0], state_fips, dtype=np.int32)\n )\n for var in get_calculated_variables(s):\n s.delete_arrays(var)\n return s\n\n# Compare SNAP for first 5 households under NC vs AK rules\nnc_sim = create_state_simulation(37) # NC\nak_sim = create_state_simulation(2) # AK\n\nnc_snap = nc_sim.calculate(\"snap\", map_to=\"household\").values[:5]\nak_snap = ak_sim.calculate(\"snap\", map_to=\"household\").values[:5]\n\nprint(\"SNAP values for first 5 households under different state rules:\")\nprint(f\" NC rules: {nc_snap}\")\nprint(f\" AK rules: {ak_snap}\")\nprint(f\" Difference: {ak_snap - nc_snap}\")"
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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}

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