diff --git a/.gitignore b/.gitignore index a7ab98c9..6fa185f6 100644 --- a/.gitignore +++ b/.gitignore @@ -30,8 +30,8 @@ docs/.ipynb_checkpoints/ ## ACA PTC state-level uprating factors !policyengine_us_data/storage/aca_ptc_multipliers_2022_2024.csv -## Raw input cache for database pipeline -policyengine_us_data/storage/calibration/raw_inputs/ +## Calibration run outputs (weights, diagnostics, packages, config) +policyengine_us_data/storage/calibration/ ## Batch processing checkpoints completed_*.txt diff --git a/Makefile b/Makefile index 20b1d223..5a6053d0 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: all format test install download upload docker documentation data calibrate publish-local-area clean build paper clean-paper presentations database database-refresh promote-database promote-dataset +.PHONY: all format test install download upload docker documentation data calibrate calibrate-build publish-local-area clean build paper clean-paper presentations database database-refresh promote-database promote-dataset HF_CLONE_DIR ?= $(HOME)/huggingface/policyengine-us-data @@ -99,7 +99,15 @@ data: download calibrate: data python -m policyengine_us_data.calibration.unified_calibration \ - --puf-dataset policyengine_us_data/storage/puf_2024.h5 + --target-config policyengine_us_data/calibration/target_config.yaml + +calibrate-build: data + python -m policyengine_us_data.calibration.unified_calibration \ + --target-config policyengine_us_data/calibration/target_config.yaml \ + --build-only + +validate-package: + python -m policyengine_us_data.calibration.validate_package publish-local-area: python policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py diff --git a/changelog_entry.yaml b/changelog_entry.yaml index e69de29b..05018210 100644 --- a/changelog_entry.yaml +++ b/changelog_entry.yaml @@ -0,0 +1,18 @@ +- bump: minor + changes: + added: + - Unified calibration pipeline with GPU-accelerated L1/L0 solver, target config YAML, and CLI package validator + - Per-state and per-county precomputation replacing per-clone Microsimulation (51 sims instead of 436) + - Parallel state, county, and clone loop processing via ProcessPoolExecutor + - Block-level takeup re-randomization with deterministic seeded draws + - Hierarchical uprating with ACA PTC state-level CSV factors and CD reconciliation + - Modal remote runner with Volume support, CUDA OOM fixes, and checkpointing + - Stacked dataset builder with sparse CD subsets and calibration block propagation + changed: + - Geography assignment now prevents clone-to-CD collisions + - County-dependent vars (aca_ptc) selectively precomputed per county; other vars use state-only path + - Target config switched to finest-grain include mode (~18K targets) + fixed: + - Cross-state cache pollution in matrix builder precomputation + - Takeup draw ordering mismatch between matrix builder and stacked builder + - At-large district geoid mismatch (7 districts had 0 estimates) diff --git a/docs/calibration.md b/docs/calibration.md new file mode 100644 index 00000000..f428c6bd --- /dev/null +++ b/docs/calibration.md @@ -0,0 +1,363 @@ +# Calibration Pipeline User's Manual + +The unified calibration pipeline reweights cloned CPS records to match administrative targets using L0-regularized optimization. This guide covers the main workflows: lightweight build-then-fit, full pipeline with PUF, and fitting from a saved package. + +## Quick Start + +```bash +# Build matrix only from stratified CPS (no PUF, no re-imputation): +python -m policyengine_us_data.calibration.unified_calibration \ + --target-config policyengine_us_data/calibration/target_config.yaml \ + --skip-source-impute \ + --skip-takeup-rerandomize \ + --build-only + +# Fit weights from a saved package: +python -m policyengine_us_data.calibration.unified_calibration \ + --package-path storage/calibration/calibration_package.pkl \ + --epochs 500 --device cuda + +# Full pipeline with PUF (build + fit in one shot): +make calibrate +``` + +## Architecture Overview + +The pipeline has two phases: + +1. **Matrix build**: Clone CPS records, assign geography, compute all target variable values, assemble a sparse calibration matrix. Optionally includes PUF cloning (doubles record count) and source re-imputation. +2. **Weight fitting** (~5-20 min on GPU): L0-regularized optimization to find household weights that reproduce administrative targets. + +The calibration package checkpoint lets you run phase 1 once and iterate on phase 2 with different hyperparameters or target selections---without rebuilding. + +### Prerequisites + +The matrix build requires two inputs from the data pipeline: + +- **Stratified CPS** (`storage/stratified_extended_cps_2024.h5`): ~12K households, built by `make data`. This is the base dataset that gets cloned. +- **Target database** (`storage/calibration/policy_data.db`): Administrative targets, built by `make database`. + +Both must exist before running calibration. The stratified CPS already contains all CPS variables needed for calibration; PUF cloning and source re-imputation are optional enhancements that happen at calibration time. + +## Workflows + +### 1. Lightweight build-then-fit (recommended for iteration) + +Build the matrix from the stratified CPS without PUF cloning or re-imputation. This is the fastest way to get a calibration package for experimentation. + +**Step 1: Build the matrix (~12K base records x 436 clones = ~5.2M columns).** + +```bash +python -m policyengine_us_data.calibration.unified_calibration \ + --target-config policyengine_us_data/calibration/target_config.yaml \ + --skip-source-impute \ + --skip-takeup-rerandomize \ + --build-only +``` + +This saves `storage/calibration/calibration_package.pkl` (default location). Use `--package-output` to specify a different path. + +**Step 2: Fit weights from the package (fast, repeatable).** + +```bash +python -m policyengine_us_data.calibration.unified_calibration \ + --package-path storage/calibration/calibration_package.pkl \ + --epochs 1000 \ + --lambda-l0 1e-8 \ + --beta 0.65 \ + --lambda-l2 1e-8 \ + --device cuda +``` + +You can re-run Step 2 as many times as you want with different hyperparameters. The expensive matrix build only happens once. + +### 2. Full pipeline with PUF + +Adding `--puf-dataset` doubles the record count (~24K base records x 436 clones = ~10.4M columns) by creating PUF-imputed copies of every CPS record. This also triggers source re-imputation unless skipped. + +**Single-pass (build + fit):** + +```bash +python -m policyengine_us_data.calibration.unified_calibration \ + --puf-dataset policyengine_us_data/storage/puf_2024.h5 \ + --target-config policyengine_us_data/calibration/target_config.yaml \ + --epochs 200 \ + --device cuda +``` + +Or equivalently: `make calibrate` + +Output: +- `storage/calibration/unified_weights.npy` --- calibrated weight vector +- `storage/calibration/unified_diagnostics.csv` --- per-target error report +- `storage/calibration/unified_run_config.json` --- full run configuration + +**Build-only (save package for later fitting):** + +```bash +python -m policyengine_us_data.calibration.unified_calibration \ + --puf-dataset policyengine_us_data/storage/puf_2024.h5 \ + --target-config policyengine_us_data/calibration/target_config.yaml \ + --build-only +``` + +Or equivalently: `make calibrate-build` + +This saves `storage/calibration/calibration_package.pkl` (default location). Use `--package-output` to specify a different path. + +Then fit from the package using the same Step 2 command from Workflow 1. + +### 3. Re-filtering a saved package + +A saved package contains **all** targets from the database (before target config filtering). You can apply a different target config at fit time: + +```bash +python -m policyengine_us_data.calibration.unified_calibration \ + --package-path storage/calibration/calibration_package.pkl \ + --target-config my_custom_config.yaml \ + --epochs 200 +``` + +This lets you experiment with which targets to include without rebuilding the matrix. + +### 4. Running on Modal (GPU cloud) + +**From a pre-built package via Modal Volume** (recommended): + +The calibration package is ~2 GB, too large to pass as a function argument. Upload it to a Modal Volume first, then reference it at runtime. + +```bash +# One-time: create volume and upload package +modal volume create calibration-data +modal volume put calibration-data \ + policyengine_us_data/storage/calibration/calibration_package.pkl \ + calibration_package.pkl + +# Fit weights (reads from volume, no inline upload) +modal run modal_app/remote_calibration_runner.py \ + --package-volume \ + --branch calibration-pipeline-improvements \ + --gpu T4 \ + --epochs 1000 \ + --beta 0.65 \ + --lambda-l0 1e-8 \ + --lambda-l2 1e-8 +``` + +To update the package on the volume after a rebuild, re-run the `modal volume put` command. + +**Full pipeline** (builds matrix from scratch on Modal): + +```bash +modal run modal_app/remote_calibration_runner.py \ + --branch calibration-pipeline-improvements \ + --gpu T4 \ + --epochs 1000 \ + --beta 0.65 \ + --lambda-l0 1e-8 \ + --lambda-l2 1e-8 \ + --target-config policyengine_us_data/calibration/target_config.yaml +``` + +The target config YAML is read from the cloned repo inside the container, so it must be committed to the branch you specify. + +### 5. Portable fitting (Kaggle, Colab, etc.) + +Transfer the package file to any environment with `scipy`, `numpy`, `pandas`, `torch`, and `l0-python` installed: + +```python +from policyengine_us_data.calibration.unified_calibration import ( + load_calibration_package, + apply_target_config, + fit_l0_weights, +) + +package = load_calibration_package("calibration_package.pkl") +targets_df = package["targets_df"] +X_sparse = package["X_sparse"] + +weights = fit_l0_weights( + X_sparse=X_sparse, + targets=targets_df["value"].values, + lambda_l0=1e-8, + epochs=500, + device="cuda", + beta=0.65, + lambda_l2=1e-8, +) +``` + +## Target Config + +The target config controls which targets reach the optimizer. It uses a YAML exclusion list: + +```yaml +exclude: + - variable: rent + geo_level: national + - variable: eitc + geo_level: district + - variable: snap + geo_level: state + domain_variable: snap # optional: further narrow the match +``` + +Each rule drops rows from the calibration matrix where **all** specified fields match. Unrecognized variables silently match nothing. + +### Fields + +| Field | Required | Values | Description | +|---|---|---|---| +| `variable` | Yes | Any variable name in `target_overview` | The calibration target variable | +| `geo_level` | Yes | `national`, `state`, `district` | Geographic aggregation level | +| `domain_variable` | No | Any domain variable in `target_overview` | Narrows match to a specific domain | + +### Default config + +The checked-in config at `policyengine_us_data/calibration/target_config.yaml` reproduces the junkyard notebook's 22 excluded target groups. It drops: + +- **13 national-level variables**: alimony, charitable deduction, child support, interest deduction, medical expense deduction, net worth, person count, real estate taxes, rent, social security dependents/survivors +- **9 district-level variables**: ACA PTC, EITC, income tax before credits, medical expense deduction, net capital gains, rental income, tax unit count, partnership/S-corp income, taxable social security + +Applying this config reduces targets from ~37K to ~21K, matching the junkyard's target selection. + +### Writing a custom config + +To experiment, copy the default and edit: + +```bash +cp policyengine_us_data/calibration/target_config.yaml my_config.yaml +# Edit my_config.yaml to add/remove exclusion rules +python -m policyengine_us_data.calibration.unified_calibration \ + --package-path storage/calibration/calibration_package.pkl \ + --target-config my_config.yaml \ + --epochs 200 +``` + +To see what variables and geo_levels are available in the database: + +```sql +SELECT DISTINCT variable, geo_level +FROM target_overview +ORDER BY variable, geo_level; +``` + +## CLI Reference + +### Core flags + +| Flag | Default | Description | +|---|---|---| +| `--dataset` | `storage/stratified_extended_cps_2024.h5` | Path to CPS h5 file | +| `--db-path` | `storage/calibration/policy_data.db` | Path to target database | +| `--output` | `storage/calibration/unified_weights.npy` | Weight output path | +| `--puf-dataset` | None | Path to PUF h5 (enables PUF cloning) | +| `--preset` | `local` | L0 preset: `local` (1e-8) or `national` (1e-4) | +| `--lambda-l0` | None | Custom L0 penalty (overrides `--preset`) | +| `--epochs` | 100 | Training epochs | +| `--device` | `cpu` | `cpu` or `cuda` | +| `--n-clones` | 436 | Number of dataset clones | +| `--seed` | 42 | Random seed for geography assignment | + +### Target selection + +| Flag | Default | Description | +|---|---|---| +| `--target-config` | None | Path to YAML exclusion config | +| `--domain-variables` | None | Comma-separated domain filter (SQL-level) | +| `--hierarchical-domains` | None | Domains for hierarchical uprating | + +### Checkpoint flags + +| Flag | Default | Description | +|---|---|---| +| `--build-only` | False | Build matrix, save package, skip fitting | +| `--package-path` | None | Load pre-built package (skip matrix build) | +| `--package-output` | Auto (when `--build-only`) | Where to save package | + +### Hyperparameter flags + +| Flag | Default | Junkyard value | Description | +|---|---|---|---| +| `--beta` | 0.35 | 0.65 | L0 gate temperature (higher = softer gates) | +| `--lambda-l2` | 1e-12 | 1e-8 | L2 regularization on weights | +| `--learning-rate` | 0.15 | 0.15 | Optimizer learning rate | + +### Skip flags + +| Flag | Description | +|---|---| +| `--skip-puf` | Skip PUF clone + QRF imputation | +| `--skip-source-impute` | Skip ACS/SIPP/SCF re-imputation | +| `--skip-takeup-rerandomize` | Skip takeup re-randomization | + +## Calibration Package Format + +The package is a pickled Python dict: + +```python +{ + "X_sparse": scipy.sparse.csr_matrix, # (n_targets, n_records) + "targets_df": pd.DataFrame, # target metadata + values + "target_names": list[str], # human-readable names + "metadata": { + "dataset_path": str, + "db_path": str, + "n_clones": int, + "n_records": int, + "seed": int, + "created_at": str, # ISO timestamp + "target_config": dict, # config used at build time + }, +} +``` + +The `targets_df` DataFrame has columns: `variable`, `geo_level`, `geographic_id`, `domain_variable`, `value`, and others from the database. + +## Validating a Package + +Before uploading a package to Modal, validate it: + +```bash +# Default package location +python -m policyengine_us_data.calibration.validate_package + +# Specific package +python -m policyengine_us_data.calibration.validate_package path/to/calibration_package.pkl + +# Strict mode: fail if any target has row_sum/target < 1% +python -m policyengine_us_data.calibration.validate_package --strict +``` + +Exit codes: **0** = pass, **1** = impossible targets, **2** = strict ratio failures. + +Validation also runs automatically after `--build-only`. + +## Hyperparameter Tuning Guide + +The three key hyperparameters control the tradeoff between target accuracy and sparsity: + +- **`beta`** (L0 gate temperature): Controls how sharply the L0 gates open/close. Higher values (0.5--0.8) give softer decisions and more exploration early in training. Lower values (0.2--0.4) give harder on/off decisions. + +- **`lambda_l0`** (via `--preset` or `--lambda-l0`): Controls how many records survive. `1e-8` (local preset) keeps millions of records for local-area analysis. `1e-4` (national preset) keeps ~50K for the web app. + +- **`lambda_l2`**: Regularizes weight magnitudes. Larger values (1e-8) prevent any single record from having extreme weight. Smaller values (1e-12) allow more weight concentration. + +### Suggested starting points + +For **local-area calibration** (millions of records): +```bash +--lambda-l0 1e-8 --beta 0.65 --lambda-l2 1e-8 --epochs 500 +``` + +For **national web app** (~50K records): +```bash +--lambda-l0 1e-4 --beta 0.35 --lambda-l2 1e-12 --epochs 200 +``` + +## Makefile Targets + +| Target | Description | +|---|---| +| `make calibrate` | Full pipeline with PUF and target config | +| `make calibrate-build` | Build-only mode (saves package, no fitting) | diff --git a/docs/calibration_matrix.ipynb b/docs/calibration_matrix.ipynb index 41497b1e..3daf7f3d 100644 --- a/docs/calibration_matrix.ipynb +++ b/docs/calibration_matrix.ipynb @@ -24,10 +24,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], - "source": "import numpy as np\nimport pandas as pd\nfrom policyengine_us import Microsimulation\nfrom policyengine_us_data.storage import STORAGE_FOLDER\nfrom policyengine_us_data.calibration.unified_matrix_builder import (\n UnifiedMatrixBuilder,\n)\nfrom policyengine_us_data.calibration.clone_and_assign import (\n assign_random_geography,\n)\nfrom policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import (\n create_target_groups,\n drop_target_groups,\n get_geo_level,\n STATE_CODES,\n)\n\ndb_path = STORAGE_FOLDER / \"calibration\" / \"policy_data.db\"\ndb_uri = f\"sqlite:///{db_path}\"\ndataset_path = STORAGE_FOLDER / \"stratified_extended_cps_2024.h5\"" + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/baogorek/envs/sep/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from policyengine_us import Microsimulation\n", + "from policyengine_us_data.storage import STORAGE_FOLDER\n", + "from policyengine_us_data.calibration.unified_matrix_builder import (\n", + " UnifiedMatrixBuilder,\n", + ")\n", + "from policyengine_us_data.calibration.clone_and_assign import (\n", + " assign_random_geography,\n", + ")\n", + "from policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import (\n", + " create_target_groups,\n", + " drop_target_groups,\n", + " get_geo_level,\n", + " STATE_CODES,\n", + ")\n", + "\n", + "db_path = STORAGE_FOLDER / \"calibration\" / \"policy_data.db\"\n", + "db_uri = f\"sqlite:///{db_path}\"\n", + "dataset_path = STORAGE_FOLDER / \"stratified_extended_cps_2024.h5\"" + ] }, { "cell_type": "code", @@ -40,7 +70,7 @@ "text": [ "Records: 11,999, Clones: 3, Total columns: 35,997\n", "Matrix shape: (1411, 35997)\n", - "Non-zero entries: 14,946\n" + "Non-zero entries: 29,425\n" ] } ], @@ -79,10 +109,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], - "source": "print(f\"Targets: {X_sparse.shape[0]}\")\nprint(f\"Columns: {X_sparse.shape[1]:,} ({N_CLONES} clones x {n_records:,} records)\")\nprint(f\"Non-zeros: {X_sparse.nnz:,}\")\nprint(f\"Density: {X_sparse.nnz / (X_sparse.shape[0] * X_sparse.shape[1]):.6f}\")\n\ngeo_levels = targets_df[\"geographic_id\"].apply(get_geo_level)\nlevel_names = {0: \"National\", 1: \"State\", 2: \"District\"}\nfor level in [0, 1, 2]:\n n = (geo_levels == level).sum()\n if n > 0:\n print(f\" {level_names[level]}: {n} targets\")" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Targets: 1411\n", + "Columns: 35,997 (3 clones x 11,999 records)\n", + "Non-zeros: 29,425\n", + "Density: 0.000579\n", + " National: 1 targets\n", + " State: 102 targets\n", + " District: 1308 targets\n" + ] + } + ], + "source": [ + "print(f\"Targets: {X_sparse.shape[0]}\")\n", + "print(f\"Columns: {X_sparse.shape[1]:,} ({N_CLONES} clones x {n_records:,} records)\")\n", + "print(f\"Non-zeros: {X_sparse.nnz:,}\")\n", + "print(f\"Density: {X_sparse.nnz / (X_sparse.shape[0] * X_sparse.shape[1]):.6f}\")\n", + "\n", + "geo_levels = targets_df[\"geographic_id\"].apply(get_geo_level)\n", + "level_names = {0: \"National\", 1: \"State\", 2: \"District\"}\n", + "for level in [0, 1, 2]:\n", + " n = (geo_levels == level).sum()\n", + " if n > 0:\n", + " print(f\" {level_names[level]}: {n} targets\")" + ] }, { "cell_type": "markdown", @@ -131,13 +187,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Row 705 has 9 non-zero columns\n", + "Row 705 has 10 non-zero columns\n", " Spans 3 clone(s)\n", - " Spans 9 unique record(s)\n", + " Spans 10 unique record(s)\n", "\n", - "First non-zero column (8000):\n", + "First non-zero column (1212):\n", " clone_idx: 0\n", - " record_idx: 8000\n", + " record_idx: 1212\n", " state_fips: 34\n", " cd_geoid: 3402\n", " value: 1.00\n" @@ -189,7 +245,7 @@ " record_idx: 42\n", " state_fips: 45\n", " cd_geoid: 4507\n", - " block_geoid: 450510801013029\n", + " block_geoid: 450410002022009\n", "\n", "This column has non-zero values in 0 target rows\n" ] @@ -334,7 +390,7 @@ "\n", "--- Group 4: District ACA PTC Tax Unit Count (436 targets) ---\n", " variable geographic_id value\n", - "tax_unit_count 1001 25064.255490\n", + "tax_unit_count 1000 25064.255490\n", "tax_unit_count 101 9794.081624\n", "tax_unit_count 102 11597.544977\n", "tax_unit_count 103 9160.097959\n", @@ -373,13 +429,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Example SNAP-receiving household: record index 23\n", - "SNAP value: $70\n", + "Example SNAP-receiving household: record index 2\n", + "SNAP value: $679\n", "\n", "Column positions across 3 clones:\n", - " col 23: TX (state=48, CD=4829) — 0 non-zero rows\n", - " col 12022: IL (state=17, CD=1708) — 0 non-zero rows\n", - " col 24021: FL (state=12, CD=1220) — 3 non-zero rows\n" + " col 2: TX (state=48, CD=4814) — 4 non-zero rows\n", + " col 12001: IN (state=18, CD=1804) — 3 non-zero rows\n", + " col 24000: PA (state=42, CD=4212) — 3 non-zero rows\n" ] } ], @@ -413,10 +469,21 @@ "output_type": "stream", "text": [ "\n", - "Clone 2 (col 24021, CD 1220):\n", - " household_count (geo=12): 1.00\n", - " snap (geo=12): 70.08\n", - " household_count (geo=1220): 1.00\n" + "Clone 0 (col 2, CD 4814):\n", + " person_count (geo=US): 3.00\n", + " household_count (geo=48): 1.00\n", + " snap (geo=48): 678.60\n", + " household_count (geo=4814): 1.00\n", + "\n", + "Clone 1 (col 12001, CD 1804):\n", + " household_count (geo=18): 1.00\n", + " snap (geo=18): 678.60\n", + " household_count (geo=1804): 1.00\n", + "\n", + "Clone 2 (col 24000, CD 4212):\n", + " household_count (geo=42): 1.00\n", + " snap (geo=42): 678.60\n", + " household_count (geo=4212): 1.00\n" ] } ], @@ -455,9 +522,9 @@ "output_type": "stream", "text": [ "Total cells: 50,791,767\n", - "Non-zero entries: 14,946\n", - "Density: 0.000294\n", - "Sparsity: 99.9706%\n" + "Non-zero entries: 29,425\n", + "Density: 0.000579\n", + "Sparsity: 99.9421%\n" ] } ], @@ -472,10 +539,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], - "source": "nnz_per_row = np.diff(X_sparse.indptr)\nprint(f\"Non-zeros per row:\")\nprint(f\" min: {nnz_per_row.min():,}\")\nprint(f\" median: {int(np.median(nnz_per_row)):,}\")\nprint(f\" mean: {nnz_per_row.mean():,.0f}\")\nprint(f\" max: {nnz_per_row.max():,}\")\n\ngeo_levels = targets_df[\"geographic_id\"].apply(get_geo_level)\nlevel_names = {0: \"National\", 1: \"State\", 2: \"District\"}\nprint(\"\\nBy geographic level:\")\nfor level in [0, 1, 2]:\n mask = (geo_levels == level).values\n if mask.any():\n vals = nnz_per_row[mask]\n print(\n f\" {level_names[level]:10s}: \"\n f\"n={mask.sum():>4d}, \"\n f\"median nnz={int(np.median(vals)):>7,}, \"\n f\"range=[{vals.min():,}, {vals.max():,}]\"\n )" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-zeros per row:\n", + " min: 0\n", + " median: 10\n", + " mean: 21\n", + " max: 3,408\n", + "\n", + "By geographic level:\n", + " National : n= 1, median nnz= 3,408, range=[3,408, 3,408]\n", + " State : n= 102, median nnz= 80, range=[10, 694]\n", + " District : n=1308, median nnz= 9, range=[0, 27]\n" + ] + } + ], + "source": [ + "nnz_per_row = np.diff(X_sparse.indptr)\n", + "print(f\"Non-zeros per row:\")\n", + "print(f\" min: {nnz_per_row.min():,}\")\n", + "print(f\" median: {int(np.median(nnz_per_row)):,}\")\n", + "print(f\" mean: {nnz_per_row.mean():,.0f}\")\n", + "print(f\" max: {nnz_per_row.max():,}\")\n", + "\n", + "geo_levels = targets_df[\"geographic_id\"].apply(get_geo_level)\n", + "level_names = {0: \"National\", 1: \"State\", 2: \"District\"}\n", + "print(\"\\nBy geographic level:\")\n", + "for level in [0, 1, 2]:\n", + " mask = (geo_levels == level).values\n", + " if mask.any():\n", + " vals = nnz_per_row[mask]\n", + " print(\n", + " f\" {level_names[level]:10s}: \"\n", + " f\"n={mask.sum():>4d}, \"\n", + " f\"median nnz={int(np.median(vals)):>7,}, \"\n", + " f\"range=[{vals.min():,}, {vals.max():,}]\"\n", + " )" + ] }, { "cell_type": "code", @@ -488,9 +593,9 @@ "text": [ "Non-zeros per clone block:\n", " clone nnz unique_states\n", - " 0 4962 50\n", - " 1 4988 50\n", - " 2 4996 50\n" + " 0 9775 51\n", + " 1 9810 51\n", + " 2 9840 51\n" ] } ], @@ -613,15 +718,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Achievable targets: 479\n", - "Impossible targets: 881\n", + "Achievable targets: 1358\n", + "Impossible targets: 2\n", "\n", "Impossible targets by (domain, variable):\n", - " aca_ptc/aca_ptc: 436\n", - " aca_ptc/tax_unit_count: 436\n", - " snap/household_count: 7\n", - " aca_ptc/person_count: 1\n", - " snap/snap: 1\n" + " aca_ptc/aca_ptc: 1\n", + " aca_ptc/tax_unit_count: 1\n" ] } ], @@ -657,11 +759,11 @@ "output_type": "stream", "text": [ "Hardest targets (lowest row_sum / target_value ratio):\n", - " snap/household_count (geo=621): ratio=0.0000, row_sum=4, target=119,148\n", - " snap/household_count (geo=3615): ratio=0.0001, row_sum=9, target=173,591\n", - " snap/snap (geo=46): ratio=0.0001, row_sum=9,421, target=180,195,817\n", - " snap/household_count (geo=3625): ratio=0.0001, row_sum=4, target=67,315\n", - " snap/household_count (geo=1702): ratio=0.0001, row_sum=6, target=97,494\n" + " aca_ptc/aca_ptc (geo=3612): ratio=0.0000, row_sum=5,439, target=376,216,522\n", + " aca_ptc/aca_ptc (geo=2508): ratio=0.0000, row_sum=2,024, target=124,980,814\n", + " aca_ptc/tax_unit_count (geo=2508): ratio=0.0000, row_sum=1, target=51,937\n", + " aca_ptc/tax_unit_count (geo=3612): ratio=0.0000, row_sum=2, target=73,561\n", + " aca_ptc/tax_unit_count (geo=1198): ratio=0.0000, row_sum=1, target=30,419\n" ] } ], @@ -692,9 +794,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Final matrix shape: (479, 35997)\n", - "Final non-zero entries: 9,944\n", - "Final density: 0.000577\n", + "Final matrix shape: (1358, 35997)\n", + "Final non-zero entries: 23,018\n", + "Final density: 0.000471\n", "\n", "This is what the optimizer receives.\n" ] @@ -747,4 +849,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/local_area_calibration_setup.ipynb b/docs/local_area_calibration_setup.ipynb index 2e8614aa..77c316b3 100644 --- a/docs/local_area_calibration_setup.ipynb +++ b/docs/local_area_calibration_setup.ipynb @@ -96,7 +96,7 @@ "output_type": "stream", "text": [ "Base dataset: 11,999 households\n", - "Example household: record_idx=8629, household_id=128694, SNAP=$18,396.00\n" + "Example household: record_idx=8629, household_id=130831, SNAP=$0.00\n" ] } ], @@ -137,9 +137,9 @@ "output_type": "stream", "text": [ "Total cloned records: 35,997\n", - "Unique states: 50\n", - "Unique CDs: 435\n", - "Unique blocks: 35508\n" + "Unique states: 51\n", + "Unique CDs: 436\n", + "Unique blocks: 35517\n" ] } ], @@ -203,8 +203,8 @@ " 8629\n", " 48\n", " TX\n", - " 4817\n", - " 481450004002026\n", + " 4816\n", + " 481410030003002\n", " \n", " \n", " 1\n", @@ -213,7 +213,7 @@ " 42\n", " PA\n", " 4201\n", - " 420171058013029\n", + " 420171018051005\n", " \n", " \n", " 2\n", @@ -222,7 +222,7 @@ " 36\n", " NY\n", " 3611\n", - " 360850208041023\n", + " 360470200002002\n", " \n", " \n", "\n", @@ -230,9 +230,9 @@ ], "text/plain": [ " clone col state_fips abbr cd_geoid block_geoid\n", - "0 0 8629 48 TX 4817 481450004002026\n", - "1 1 20628 42 PA 4201 420171058013029\n", - "2 2 32627 36 NY 3611 360850208041023" + "0 0 8629 48 TX 4816 481410030003002\n", + "1 1 20628 42 PA 4201 420171018051005\n", + "2 2 32627 36 NY 3611 360470200002002" ] }, "execution_count": 4, @@ -280,13 +280,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Global block distribution: 5,765,442 blocks\n", + "Global block distribution: 5,769,942 blocks\n", "Top 5 states by total probability:\n", - " CA (6): 11.954%\n", - " TX (48): 8.736%\n", - " FL (12): 6.437%\n", - " NY (36): 5.977%\n", - " PA (42): 3.908%\n" + " CA (6): 11.927%\n", + " TX (48): 8.716%\n", + " FL (12): 6.422%\n", + " NY (36): 5.963%\n", + " PA (42): 3.899%\n" ] } ], @@ -327,10 +327,10 @@ "output_type": "stream", "text": [ "Example household (record_idx=8629):\n", - " Original state: NC (37)\n", + " Original state: CA (6)\n", " Clone 0 state: TX (48)\n", - " Original SNAP: $18,396.00\n", - " Clone 0 SNAP: $18,396.00\n" + " Original SNAP: $0.00\n", + " Clone 0 SNAP: $0.00\n" ] } ], @@ -410,31 +410,31 @@ " 0\n", " TX\n", " 48\n", - " $18,396.00\n", + " $0.00\n", " \n", " \n", " 1\n", " 1\n", " PA\n", " 42\n", - " $18,396.00\n", + " $0.00\n", " \n", " \n", " 2\n", " 2\n", " NY\n", " 36\n", - " $18,396.00\n", + " $0.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " clone state state_fips SNAP\n", - "0 0 TX 48 $18,396.00\n", - "1 1 PA 42 $18,396.00\n", - "2 2 NY 36 $18,396.00" + " clone state state_fips SNAP\n", + "0 0 TX 48 $0.00\n", + "1 1 PA 42 $0.00\n", + "2 2 NY 36 $0.00" ] }, "execution_count": 7, @@ -499,10 +499,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Unique states mapped: 50\n", - "Unique CDs mapped: 435\n", + "Unique states mapped: 51\n", + "Unique CDs mapped: 436\n", "\n", - "Columns per state: min=62, median=494, max=4311\n" + "Columns per state: min=63, median=490, max=4299\n" ] } ], @@ -539,9 +539,9 @@ "text": [ "Example household clone visibility:\n", "\n", - "Clone 0 (TX, CD 4817):\n", + "Clone 0 (TX, CD 4816):\n", " Visible to TX state targets: col 8629 in state_to_cols[48]? True\n", - " Visible to CD 4817 targets: col 8629 in cd_to_cols['4817']? True\n", + " Visible to CD 4816 targets: col 8629 in cd_to_cols['4816']? True\n", " Visible to NC (37) targets: False\n", "\n", "Clone 1 (PA, CD 4201):\n", @@ -612,7 +612,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "8 takeup variables:\n", + "9 takeup variables:\n", "\n", " takes_up_snap_if_eligible entity=spm_unit rate=82.00%\n", " takes_up_aca_if_eligible entity=tax_unit rate=67.20%\n", @@ -621,7 +621,8 @@ " takes_up_early_head_start_if_eligible entity=person rate=9.00%\n", " takes_up_ssi_if_eligible entity=person rate=50.00%\n", " would_file_taxes_voluntarily entity=tax_unit rate=5.00%\n", - " takes_up_medicaid_if_eligible entity=person rate=dict (51 entries)\n" + " takes_up_medicaid_if_eligible entity=person rate=dict (51 entries)\n", + " takes_up_tanf_if_eligible entity=spm_unit rate=22.00%\n" ] } ], @@ -708,14 +709,15 @@ "text": [ "Takeup rates before/after re-randomization (clone 0):\n", "\n", - " takes_up_snap_if_eligible before=82.333% after=82.381%\n", - " takes_up_aca_if_eligible before=66.718% after=67.486%\n", - " takes_up_dc_ptc before=31.483% after=32.044%\n", - " takes_up_head_start_if_eligible before=29.963% after=29.689%\n", - " takes_up_early_head_start_if_eligible before=8.869% after=8.721%\n", - " takes_up_ssi_if_eligible before=100.000% after=49.776%\n", - " would_file_taxes_voluntarily before=0.000% after=4.905%\n", - " takes_up_medicaid_if_eligible before=84.496% after=80.051%\n" + " takes_up_snap_if_eligible before=82.116% after=82.364%\n", + " takes_up_aca_if_eligible before=67.115% after=67.278%\n", + " takes_up_dc_ptc before=31.673% after=31.534%\n", + " takes_up_head_start_if_eligible before=100.000% after=29.852%\n", + " takes_up_early_head_start_if_eligible before=100.000% after=8.904%\n", + " takes_up_ssi_if_eligible before=100.000% after=49.504%\n", + " would_file_taxes_voluntarily before=0.000% after=5.115%\n", + " takes_up_medicaid_if_eligible before=84.868% after=80.354%\n", + " takes_up_tanf_if_eligible before=100.000% after=21.991%\n" ] } ], @@ -801,11 +803,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-13 17:11:22,384 - INFO - Processing clone 1/3 (cols 0-11998, 50 unique states)...\n", - "2026-02-13 17:11:23,509 - INFO - Processing clone 2/3 (cols 11999-23997, 50 unique states)...\n", - "2026-02-13 17:11:24,645 - INFO - Processing clone 3/3 (cols 23998-35996, 50 unique states)...\n", - "2026-02-13 17:11:25,769 - INFO - Assembling matrix from 3 clones...\n", - "2026-02-13 17:11:25,771 - INFO - Matrix: 538 targets x 35997 cols, 14946 nnz\n" + "2026-02-20 15:34:21,531 - INFO - Per-state precomputation: 51 states, 1 hh vars, 1 constraint vars\n", + "2026-02-20 15:34:22,137 - INFO - State 1/51 complete\n", + "2026-02-20 15:34:27,750 - INFO - State 10/51 complete\n", + "2026-02-20 15:34:34,205 - INFO - State 20/51 complete\n", + "2026-02-20 15:34:40,885 - INFO - State 30/51 complete\n", + "2026-02-20 15:34:47,174 - INFO - State 40/51 complete\n", + "2026-02-20 15:34:53,723 - INFO - State 50/51 complete\n", + "2026-02-20 15:34:54,415 - INFO - Per-state precomputation done: 51 states\n", + "2026-02-20 15:34:54,419 - INFO - Assembling clone 1/3 (cols 0-11998, 51 unique states)...\n", + "2026-02-20 15:34:54,516 - INFO - Assembling matrix from 3 clones...\n", + "2026-02-20 15:34:54,517 - INFO - Matrix: 538 targets x 35997 cols, 19140 nnz\n" ] }, { @@ -813,8 +821,8 @@ "output_type": "stream", "text": [ "Matrix shape: (538, 35997)\n", - "Non-zero entries: 14,946\n", - "Density: 0.000772\n" + "Non-zero entries: 19,140\n", + "Density: 0.000988\n" ] } ], @@ -848,18 +856,9 @@ "text": [ "Example household non-zero pattern across clones:\n", "\n", - "Clone 0 (TX, CD 4817): 3 non-zero rows\n", - " row 39: household_count (geo=48): 1.00\n", - " row 90: snap (geo=48): 18396.00\n", - " row 410: household_count (geo=4817): 1.00\n", - "Clone 1 (PA, CD 4201): 3 non-zero rows\n", - " row 34: household_count (geo=42): 1.00\n", - " row 85: snap (geo=42): 18396.00\n", - " row 358: household_count (geo=4201): 1.00\n", - "Clone 2 (NY, CD 3611): 3 non-zero rows\n", - " row 27: household_count (geo=36): 1.00\n", - " row 78: snap (geo=36): 18396.00\n", - " row 292: household_count (geo=3611): 1.00\n" + "Clone 0 (TX, CD 4816): 0 non-zero rows\n", + "Clone 1 (PA, CD 4201): 0 non-zero rows\n", + "Clone 2 (NY, CD 3611): 0 non-zero rows\n" ] } ], @@ -993,6 +992,7 @@ "Extracted weights for 2 CDs from full weight matrix\n", "Total active household-CD pairs: 277\n", "Total weight in W matrix: 281\n", + "Warning: No rent data for CD 201, using geoadj=1.0\n", "Processing CD 201 (2/2)...\n" ] }, @@ -1000,10 +1000,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2026-02-13 17:11:40,873 - INFO - HTTP Request: GET https://huggingface.co/api/models/policyengine/policyengine-us-data \"HTTP/1.1 200 OK\"\n", - "2026-02-13 17:11:40,899 - INFO - HTTP Request: HEAD https://huggingface.co/policyengine/policyengine-us-data/resolve/main/enhanced_cps_2024.h5 \"HTTP/1.1 302 Found\"\n", - "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", - "2026-02-13 17:11:40,899 - WARNING - Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" + "2026-02-20 15:35:04,090 - INFO - HTTP Request: GET https://huggingface.co/api/models/policyengine/policyengine-us-data \"HTTP/1.1 200 OK\"\n", + "2026-02-20 15:35:04,123 - INFO - HTTP Request: HEAD https://huggingface.co/policyengine/policyengine-us-data/resolve/main/enhanced_cps_2024.h5 \"HTTP/1.1 302 Found\"\n" ] }, { @@ -1013,7 +1011,7 @@ "\n", "Combining 2 CD DataFrames...\n", "Total households across all CDs: 277\n", - "Combined DataFrame shape: (726, 222)\n", + "Combined DataFrame shape: (716, 219)\n", "\n", "Reindexing all entity IDs using 25k ranges per CD...\n", " Created 277 unique households across 2 CDs\n", @@ -1022,12 +1020,12 @@ " Reindexing SPM units...\n", " Reindexing marital units...\n", " Reindexing families...\n", - " Final persons: 726\n", + " Final persons: 716\n", " Final households: 277\n", - " Final tax units: 373\n", - " Final SPM units: 291\n", - " Final marital units: 586\n", - " Final families: 309\n", + " Final tax units: 387\n", + " Final SPM units: 290\n", + " Final marital units: 587\n", + " Final families: 318\n", "\n", "Weights in combined_df AFTER reindexing:\n", " HH weight sum: 0.00M\n", @@ -1035,8 +1033,8 @@ " Ratio: 1.00\n", "\n", "Overflow check:\n", - " Max person ID after reindexing: 5,025,335\n", - " Max person ID × 100: 502,533,500\n", + " Max person ID after reindexing: 5,025,365\n", + " Max person ID × 100: 502,536,500\n", " int32 max: 2,147,483,647\n", " ✓ No overflow risk!\n", "\n", @@ -1044,15 +1042,15 @@ "Building simulation from Dataset...\n", "\n", "Saving to calibration_output/results.h5...\n", - "Found 175 input variables to save\n", - "Variables saved: 218\n", - "Variables skipped: 3763\n", + "Found 172 input variables to save\n", + "Variables saved: 215\n", + "Variables skipped: 3825\n", "Sparse CD-stacked dataset saved successfully!\n", "Household mapping saved to calibration_output/mappings/results_household_mapping.csv\n", "\n", "Verifying saved file...\n", " Final households: 277\n", - " Final persons: 726\n", + " Final persons: 716\n", " Total population (from household weights): 281\n" ] }, @@ -1089,17 +1087,17 @@ "text": [ "Stacked dataset: 277 households\n", "\n", - "Example household (original_id=128694) in mapping:\n", + "Example household (original_id=130831) in mapping:\n", "\n", " new_household_id original_household_id congressional_district state_fips\n", - " 108 128694 201 2\n", - " 25097 128694 3701 37\n", + " 108 130831 201 2\n", + " 25097 130831 3701 37\n", "\n", "In stacked dataset:\n", "\n", - " household_id congressional_district_geoid household_weight state_fips snap\n", - " 108 201 3.5 2 23640.0\n", - " 25097 3701 2.5 37 18396.0\n" + " household_id congressional_district_geoid household_weight state_fips snap\n", + " 108 201 3.5 2 0.0\n", + " 25097 3701 2.5 37 0.0\n" ] } ], diff --git a/modal_app/local_area.py b/modal_app/local_area.py index 92e06833..9d474b42 100644 --- a/modal_app/local_area.py +++ b/modal_app/local_area.py @@ -254,18 +254,18 @@ def validate_staging(branch: str, version: str) -> Dict: @app.function( image=image, - secrets=[hf_secret, gcp_secret], + secrets=[hf_secret], volumes={VOLUME_MOUNT: staging_volume}, memory=8192, timeout=14400, ) def upload_to_staging(branch: str, version: str, manifest: Dict) -> str: """ - Upload files to GCS (production) and HuggingFace (staging only). + Upload files to HuggingFace staging only. + GCS is updated during promote_publish, not here. Promote must be run separately via promote_publish. """ - setup_gcp_credentials() setup_repo(branch) manifest_json = json.dumps(manifest) @@ -280,10 +280,7 @@ def upload_to_staging(branch: str, version: str, manifest: Dict) -> str: import json from pathlib import Path from policyengine_us_data.utils.manifest import verify_manifest -from policyengine_us_data.utils.data_upload import ( - upload_local_area_file, - upload_to_staging_hf, -) +from policyengine_us_data.utils.data_upload import upload_to_staging_hf manifest = json.loads('''{manifest_json}''') version = "{version}" @@ -305,20 +302,6 @@ def upload_to_staging(branch: str, version: str, manifest: Dict) -> str: local_path = version_dir / rel_path files_with_paths.append((local_path, rel_path)) -# Upload to GCS (direct to production paths) -print(f"Uploading {{len(files_with_paths)}} files to GCS...") -gcs_count = 0 -for local_path, rel_path in files_with_paths: - subdirectory = str(Path(rel_path).parent) - upload_local_area_file( - str(local_path), - subdirectory, - version=version, - skip_hf=True, - ) - gcs_count += 1 -print(f"Uploaded {{gcs_count}} files to GCS") - # Upload to HuggingFace staging/ print(f"Uploading {{len(files_with_paths)}} files to HuggingFace staging/...") hf_count = upload_to_staging_hf(files_with_paths, version) @@ -336,24 +319,26 @@ def upload_to_staging(branch: str, version: str, manifest: Dict) -> str: return ( f"Staged version {version} with {len(manifest['files'])} files. " - f"Run promote workflow to publish to HuggingFace production." + f"Run promote workflow to publish to HuggingFace production and GCS." ) @app.function( image=image, - secrets=[hf_secret], + secrets=[hf_secret, gcp_secret], volumes={VOLUME_MOUNT: staging_volume}, memory=4096, timeout=3600, ) def promote_publish(branch: str = "main", version: str = "") -> str: """ - Promote staged files from HF staging/ to production paths, then cleanup. + Promote staged files from HF staging/ to production paths, + upload to GCS, then cleanup HF staging. Reads the manifest from the Modal staging volume to determine which files to promote. """ + setup_gcp_credentials() setup_repo(branch) staging_dir = Path(VOLUME_MOUNT) @@ -379,17 +364,34 @@ def promote_publish(branch: str = "main", version: str = "") -> str: "-c", f""" import json +from pathlib import Path from policyengine_us_data.utils.data_upload import ( promote_staging_to_production_hf, cleanup_staging_hf, + upload_local_area_file, ) rel_paths = json.loads('''{rel_paths_json}''') version = "{version}" +version_dir = Path("{VOLUME_MOUNT}") / version print(f"Promoting {{len(rel_paths)}} files from staging/ to production...") promoted = promote_staging_to_production_hf(rel_paths, version) -print(f"Promoted {{promoted}} files to production") +print(f"Promoted {{promoted}} files to HuggingFace production") + +print(f"Uploading {{len(rel_paths)}} files to GCS...") +gcs_count = 0 +for rel_path in rel_paths: + local_path = version_dir / rel_path + subdirectory = str(Path(rel_path).parent) + upload_local_area_file( + str(local_path), + subdirectory, + version=version, + skip_hf=True, + ) + gcs_count += 1 +print(f"Uploaded {{gcs_count}} files to GCS") print("Cleaning up staging/...") cleaned = cleanup_staging_hf(rel_paths, version) diff --git a/modal_app/remote_calibration_runner.py b/modal_app/remote_calibration_runner.py index 689d245d..fa88abfd 100644 --- a/modal_app/remote_calibration_runner.py +++ b/modal_app/remote_calibration_runner.py @@ -5,6 +5,9 @@ app = modal.App("policyengine-us-data-fit-weights") hf_secret = modal.Secret.from_name("huggingface-token") +calibration_vol = modal.Volume.from_name( + "calibration-data", create_if_missing=True +) image = ( modal.Image.debian_slim(python_version="3.11") @@ -15,16 +18,105 @@ REPO_URL = "https://github.com/PolicyEngine/policyengine-us-data.git" -def _fit_weights_impl(branch: str, epochs: int) -> dict: - """Shared implementation for weight fitting.""" +def _run_streaming(cmd, env=None, label=""): + """Run a subprocess, streaming output line-by-line. + + Returns (returncode, captured_stdout_lines). + """ + proc = subprocess.Popen( + cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + bufsize=1, + env=env, + ) + lines = [] + for line in proc.stdout: + line = line.rstrip("\n") + if label: + print(f"[{label}] {line}", flush=True) + else: + print(line, flush=True) + lines.append(line) + proc.wait() + return proc.returncode, lines + + +def _clone_and_install(branch: str): + """Clone the repo and install dependencies.""" os.chdir("/root") subprocess.run(["git", "clone", "-b", branch, REPO_URL], check=True) os.chdir("policyengine-us-data") - subprocess.run(["uv", "sync", "--extra", "l0"], check=True) - print("Downloading calibration inputs from HuggingFace...") - download_result = subprocess.run( + +def _append_hyperparams( + cmd, beta, lambda_l0, lambda_l2, learning_rate, log_freq=None +): + """Append optional hyperparameter flags to a command list.""" + if beta is not None: + cmd.extend(["--beta", str(beta)]) + if lambda_l0 is not None: + cmd.extend(["--lambda-l0", str(lambda_l0)]) + if lambda_l2 is not None: + cmd.extend(["--lambda-l2", str(lambda_l2)]) + if learning_rate is not None: + cmd.extend(["--learning-rate", str(learning_rate)]) + if log_freq is not None: + cmd.extend(["--log-freq", str(log_freq)]) + + +def _collect_outputs(cal_lines): + """Extract weights and log bytes from calibration output lines.""" + output_path = None + log_path = None + cal_log_path = None + for line in cal_lines: + if "OUTPUT_PATH:" in line: + output_path = line.split("OUTPUT_PATH:")[1].strip() + elif "CAL_LOG_PATH:" in line: + cal_log_path = line.split("CAL_LOG_PATH:")[1].strip() + elif "LOG_PATH:" in line: + log_path = line.split("LOG_PATH:")[1].strip() + + with open(output_path, "rb") as f: + weights_bytes = f.read() + + log_bytes = None + if log_path: + with open(log_path, "rb") as f: + log_bytes = f.read() + + cal_log_bytes = None + if cal_log_path: + with open(cal_log_path, "rb") as f: + cal_log_bytes = f.read() + + return { + "weights": weights_bytes, + "log": log_bytes, + "cal_log": cal_log_bytes, + } + + +def _fit_weights_impl( + branch: str, + epochs: int, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + """Full pipeline: download data, build matrix, fit weights.""" + _clone_and_install(branch) + + print("Downloading calibration inputs from HuggingFace...", flush=True) + dl_rc, dl_lines = _run_streaming( [ "uv", "run", @@ -36,66 +128,126 @@ def _fit_weights_impl(branch: str, epochs: int) -> dict: "print(f\"DB: {paths['database']}\"); " "print(f\"DATASET: {paths['dataset']}\")", ], - capture_output=True, - text=True, env=os.environ.copy(), + label="download", ) - print(download_result.stdout) - if download_result.stderr: - print("Download STDERR:", download_result.stderr) - if download_result.returncode != 0: - raise RuntimeError(f"Download failed: {download_result.returncode}") + if dl_rc != 0: + raise RuntimeError(f"Download failed with code {dl_rc}") db_path = dataset_path = None - for line in download_result.stdout.split("\n"): - if line.startswith("DB:"): + for line in dl_lines: + if "DB:" in line: db_path = line.split("DB:")[1].strip() - elif line.startswith("DATASET:"): + elif "DATASET:" in line: dataset_path = line.split("DATASET:")[1].strip() script_path = "policyengine_us_data/calibration/unified_calibration.py" - result = subprocess.run( - [ - "uv", - "run", - "python", - script_path, - "--device", - "cuda", - "--epochs", - str(epochs), - "--db-path", - db_path, - "--dataset", - dataset_path, - ], - capture_output=True, - text=True, + cmd = [ + "uv", + "run", + "python", + script_path, + "--device", + "cuda", + "--epochs", + str(epochs), + "--db-path", + db_path, + "--dataset", + dataset_path, + ] + if target_config: + cmd.extend(["--target-config", target_config]) + if skip_county: + cmd.append("--skip-county") + if workers > 1: + cmd.extend(["--workers", str(workers)]) + _append_hyperparams( + cmd, beta, lambda_l0, lambda_l2, learning_rate, log_freq + ) + + cal_rc, cal_lines = _run_streaming( + cmd, env=os.environ.copy(), + label="calibrate", ) - print(result.stdout) - if result.stderr: - print("STDERR:", result.stderr) - if result.returncode != 0: - raise RuntimeError(f"Script failed with code {result.returncode}") + if cal_rc != 0: + raise RuntimeError(f"Script failed with code {cal_rc}") - output_path = None - log_path = None - for line in result.stdout.split("\n"): - if "OUTPUT_PATH:" in line: - output_path = line.split("OUTPUT_PATH:")[1].strip() - elif "LOG_PATH:" in line: - log_path = line.split("LOG_PATH:")[1].strip() + return _collect_outputs(cal_lines) - with open(output_path, "rb") as f: - weights_bytes = f.read() - log_bytes = None - if log_path: - with open(log_path, "rb") as f: - log_bytes = f.read() +def _fit_from_package_impl( + branch: str, + epochs: int, + package_bytes: bytes = None, + volume_package_path: str = None, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, +) -> dict: + """Fit weights from a pre-built calibration package.""" + _clone_and_install(branch) + + pkg_path = "/root/calibration_package.pkl" + if volume_package_path: + import shutil + + shutil.copy(volume_package_path, pkg_path) + size = os.path.getsize(pkg_path) + print( + f"Copied package from volume ({size:,} bytes) to {pkg_path}", + flush=True, + ) + elif package_bytes: + with open(pkg_path, "wb") as f: + f.write(package_bytes) + print( + f"Wrote calibration package ({len(package_bytes)} bytes) " + f"to {pkg_path}", + flush=True, + ) + else: + raise ValueError( + "Either package_bytes or volume_package_path required" + ) - return {"weights": weights_bytes, "log": log_bytes} + script_path = "policyengine_us_data/calibration/unified_calibration.py" + cmd = [ + "uv", + "run", + "python", + script_path, + "--device", + "cuda", + "--epochs", + str(epochs), + "--package-path", + pkg_path, + ] + if target_config: + cmd.extend(["--target-config", target_config]) + _append_hyperparams( + cmd, beta, lambda_l0, lambda_l2, learning_rate, log_freq + ) + + print(f"Running command: {' '.join(cmd)}", flush=True) + + cal_rc, cal_lines = _run_streaming( + cmd, + env=os.environ.copy(), + label="calibrate", + ) + if cal_rc != 0: + raise RuntimeError(f"Script failed with code {cal_rc}") + + return _collect_outputs(cal_lines) + + +# --- Full pipeline GPU functions --- @app.function( @@ -106,8 +258,30 @@ def _fit_weights_impl(branch: str, epochs: int) -> dict: gpu="T4", timeout=14400, ) -def fit_weights_t4(branch: str = "main", epochs: int = 200) -> dict: - return _fit_weights_impl(branch, epochs) +def fit_weights_t4( + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + return _fit_weights_impl( + branch, + epochs, + target_config, + beta, + lambda_l0, + lambda_l2, + learning_rate, + log_freq, + skip_county=skip_county, + workers=workers, + ) @app.function( @@ -118,8 +292,30 @@ def fit_weights_t4(branch: str = "main", epochs: int = 200) -> dict: gpu="A10", timeout=14400, ) -def fit_weights_a10(branch: str = "main", epochs: int = 200) -> dict: - return _fit_weights_impl(branch, epochs) +def fit_weights_a10( + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + return _fit_weights_impl( + branch, + epochs, + target_config, + beta, + lambda_l0, + lambda_l2, + learning_rate, + log_freq, + skip_county=skip_county, + workers=workers, + ) @app.function( @@ -130,8 +326,30 @@ def fit_weights_a10(branch: str = "main", epochs: int = 200) -> dict: gpu="A100-40GB", timeout=14400, ) -def fit_weights_a100_40(branch: str = "main", epochs: int = 200) -> dict: - return _fit_weights_impl(branch, epochs) +def fit_weights_a100_40( + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + return _fit_weights_impl( + branch, + epochs, + target_config, + beta, + lambda_l0, + lambda_l2, + learning_rate, + log_freq, + skip_county=skip_county, + workers=workers, + ) @app.function( @@ -142,8 +360,30 @@ def fit_weights_a100_40(branch: str = "main", epochs: int = 200) -> dict: gpu="A100-80GB", timeout=14400, ) -def fit_weights_a100_80(branch: str = "main", epochs: int = 200) -> dict: - return _fit_weights_impl(branch, epochs) +def fit_weights_a100_80( + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + return _fit_weights_impl( + branch, + epochs, + target_config, + beta, + lambda_l0, + lambda_l2, + learning_rate, + log_freq, + skip_county=skip_county, + workers=workers, + ) @app.function( @@ -154,8 +394,30 @@ def fit_weights_a100_80(branch: str = "main", epochs: int = 200) -> dict: gpu="H100", timeout=14400, ) -def fit_weights_h100(branch: str = "main", epochs: int = 200) -> dict: - return _fit_weights_impl(branch, epochs) +def fit_weights_h100( + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + skip_county: bool = True, + workers: int = 1, +) -> dict: + return _fit_weights_impl( + branch, + epochs, + target_config, + beta, + lambda_l0, + lambda_l2, + learning_rate, + log_freq, + skip_county=skip_county, + workers=workers, + ) GPU_FUNCTIONS = { @@ -167,22 +429,273 @@ def fit_weights_h100(branch: str = "main", epochs: int = 200) -> dict: } +# --- Package-path GPU functions --- + + +@app.function( + image=image, + memory=32768, + cpu=4.0, + gpu="T4", + timeout=14400, + volumes={"/calibration-data": calibration_vol}, +) +def fit_from_package_t4( + package_bytes: bytes = None, + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + volume_package_path: str = None, +) -> dict: + return _fit_from_package_impl( + branch, + epochs, + package_bytes=package_bytes, + volume_package_path=volume_package_path, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + + +@app.function( + image=image, + memory=32768, + cpu=4.0, + gpu="A10", + timeout=14400, + volumes={"/calibration-data": calibration_vol}, +) +def fit_from_package_a10( + package_bytes: bytes = None, + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + volume_package_path: str = None, +) -> dict: + return _fit_from_package_impl( + branch, + epochs, + package_bytes=package_bytes, + volume_package_path=volume_package_path, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + + +@app.function( + image=image, + memory=32768, + cpu=4.0, + gpu="A100-40GB", + timeout=14400, + volumes={"/calibration-data": calibration_vol}, +) +def fit_from_package_a100_40( + package_bytes: bytes = None, + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + volume_package_path: str = None, +) -> dict: + return _fit_from_package_impl( + branch, + epochs, + package_bytes=package_bytes, + volume_package_path=volume_package_path, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + + +@app.function( + image=image, + memory=32768, + cpu=4.0, + gpu="A100-80GB", + timeout=14400, + volumes={"/calibration-data": calibration_vol}, +) +def fit_from_package_a100_80( + package_bytes: bytes = None, + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + volume_package_path: str = None, +) -> dict: + return _fit_from_package_impl( + branch, + epochs, + package_bytes=package_bytes, + volume_package_path=volume_package_path, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + + +@app.function( + image=image, + memory=32768, + cpu=4.0, + gpu="H100", + timeout=14400, + volumes={"/calibration-data": calibration_vol}, +) +def fit_from_package_h100( + package_bytes: bytes = None, + branch: str = "main", + epochs: int = 200, + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + volume_package_path: str = None, +) -> dict: + return _fit_from_package_impl( + branch, + epochs, + package_bytes=package_bytes, + volume_package_path=volume_package_path, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + + +PACKAGE_GPU_FUNCTIONS = { + "T4": fit_from_package_t4, + "A10": fit_from_package_a10, + "A100-40GB": fit_from_package_a100_40, + "A100-80GB": fit_from_package_a100_80, + "H100": fit_from_package_h100, +} + + +VOLUME_MOUNT = "/calibration-data" + + @app.local_entrypoint() def main( branch: str = "main", epochs: int = 200, gpu: str = "T4", output: str = "calibration_weights.npy", - log_output: str = "calibration_log.csv", + log_output: str = "unified_diagnostics.csv", + target_config: str = None, + beta: float = None, + lambda_l0: float = None, + lambda_l2: float = None, + learning_rate: float = None, + log_freq: int = None, + package_path: str = None, + package_volume: bool = False, + county_level: bool = False, + workers: int = 1, ): if gpu not in GPU_FUNCTIONS: raise ValueError( - f"Unknown GPU: {gpu}. Choose from: {list(GPU_FUNCTIONS.keys())}" + f"Unknown GPU: {gpu}. " + f"Choose from: {list(GPU_FUNCTIONS.keys())}" ) - print(f"Running with GPU: {gpu}, epochs: {epochs}, branch: {branch}") - func = GPU_FUNCTIONS[gpu] - result = func.remote(branch=branch, epochs=epochs) + if package_volume: + vol_path = f"{VOLUME_MOUNT}/calibration_package.pkl" + print( + f"Using package from Modal volume at {vol_path}", + flush=True, + ) + func = PACKAGE_GPU_FUNCTIONS[gpu] + result = func.remote( + branch=branch, + epochs=epochs, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + volume_package_path=vol_path, + ) + elif package_path: + print(f"Reading package from {package_path}...", flush=True) + with open(package_path, "rb") as f: + package_bytes = f.read() + print( + f"Uploading package ({len(package_bytes)} bytes) " + f"to {gpu} on Modal...", + flush=True, + ) + func = PACKAGE_GPU_FUNCTIONS[gpu] + result = func.remote( + package_bytes=package_bytes, + branch=branch, + epochs=epochs, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + ) + else: + print( + f"Running full pipeline with GPU: {gpu}, " + f"epochs: {epochs}, branch: {branch}", + flush=True, + ) + func = GPU_FUNCTIONS[gpu] + result = func.remote( + branch=branch, + epochs=epochs, + target_config=target_config, + beta=beta, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + skip_county=not county_level, + workers=workers, + ) with open(output, "wb") as f: f.write(result["weights"]) @@ -191,4 +704,10 @@ def main( if result["log"]: with open(log_output, "wb") as f: f.write(result["log"]) - print(f"Calibration log saved to: {log_output}") + print(f"Diagnostics log saved to: {log_output}") + + if result.get("cal_log"): + cal_log_output = "calibration_log.csv" + with open(cal_log_output, "wb") as f: + f.write(result["cal_log"]) + print(f"Calibration log saved to: {cal_log_output}") diff --git a/policyengine_us_data/calibration/clone_and_assign.py b/policyengine_us_data/calibration/clone_and_assign.py index 9aa64cbb..e25af7d0 100644 --- a/policyengine_us_data/calibration/clone_and_assign.py +++ b/policyengine_us_data/calibration/clone_and_assign.py @@ -23,6 +23,7 @@ class GeographyAssignment: block_geoid: np.ndarray # str array, 15-char block GEOIDs cd_geoid: np.ndarray # str array of CD GEOIDs + county_fips: np.ndarray # str array of 5-char county FIPS state_fips: np.ndarray # int array of 2-digit state FIPS n_records: int n_clones: int @@ -52,7 +53,7 @@ def load_global_block_distribution(): df = pd.read_csv(csv_path, dtype={"block_geoid": str}) block_geoids = df["block_geoid"].values - cd_geoids = df["cd_geoid"].astype(str).values + cd_geoids = np.array(df["cd_geoid"].astype(str).tolist()) state_fips = np.array([int(b[:2]) for b in block_geoids]) probs = df["probability"].values.astype(np.float64) @@ -88,11 +89,44 @@ def assign_random_geography( n_total = n_records * n_clones rng = np.random.default_rng(seed) - indices = rng.choice(len(blocks), size=n_total, p=probs) + indices = np.empty(n_total, dtype=np.int64) + + # Clone 0: unrestricted draw + indices[:n_records] = rng.choice(len(blocks), size=n_records, p=probs) + + assigned_cds = np.empty((n_clones, n_records), dtype=object) + assigned_cds[0] = cds[indices[:n_records]] + + for clone_idx in range(1, n_clones): + start = clone_idx * n_records + clone_indices = rng.choice(len(blocks), size=n_records, p=probs) + clone_cds = cds[clone_indices] + + collisions = np.zeros(n_records, dtype=bool) + for prev in range(clone_idx): + collisions |= clone_cds == assigned_cds[prev] + + for _ in range(50): + n_bad = collisions.sum() + if n_bad == 0: + break + clone_indices[collisions] = rng.choice( + len(blocks), size=n_bad, p=probs + ) + clone_cds = cds[clone_indices] + collisions = np.zeros(n_records, dtype=bool) + for prev in range(clone_idx): + collisions |= clone_cds == assigned_cds[prev] + + indices[start : start + n_records] = clone_indices + assigned_cds[clone_idx] = clone_cds + + assigned_blocks = blocks[indices] return GeographyAssignment( - block_geoid=blocks[indices], + block_geoid=assigned_blocks, cd_geoid=cds[indices], + county_fips=np.array([b[:5] for b in assigned_blocks]), state_fips=states[indices], n_records=n_records, n_clones=n_clones, @@ -124,6 +158,7 @@ def double_geography_for_puf( new_blocks = [] new_cds = [] + new_counties = [] new_states = [] for c in range(n_clones): @@ -131,14 +166,17 @@ def double_geography_for_puf( end = start + n_old clone_blocks = geography.block_geoid[start:end] clone_cds = geography.cd_geoid[start:end] + clone_counties = geography.county_fips[start:end] clone_states = geography.state_fips[start:end] new_blocks.append(np.concatenate([clone_blocks, clone_blocks])) new_cds.append(np.concatenate([clone_cds, clone_cds])) + new_counties.append(np.concatenate([clone_counties, clone_counties])) new_states.append(np.concatenate([clone_states, clone_states])) return GeographyAssignment( block_geoid=np.concatenate(new_blocks), cd_geoid=np.concatenate(new_cds), + county_fips=np.concatenate(new_counties), state_fips=np.concatenate(new_states), n_records=n_new, n_clones=n_clones, diff --git a/policyengine_us_data/calibration/target_config.yaml b/policyengine_us_data/calibration/target_config.yaml new file mode 100644 index 00000000..e050fc4e --- /dev/null +++ b/policyengine_us_data/calibration/target_config.yaml @@ -0,0 +1,79 @@ +# Target config curated by achievability analysis. +# Dropped variables where per-household dollar values in extended CPS +# are 5-27x too high (needed_w < 2), making them irreconcilable with +# count targets (needed_w ~26). See achievability_ratio analysis. +# +# Dropped district: salt, tax_exempt_interest_income, dividend_income, +# income_tax, qualified_dividend_income, taxable_interest_income, +# adjusted_gross_income, qualified_business_income_deduction, +# taxable_ira_distributions +# Dropped national: income_tax_positive, traditional_ira_contributions + +include: + # === DISTRICT — count targets === + - variable: person_count + geo_level: district + - variable: household_count + geo_level: district + + # === DISTRICT — dollar targets (needed_w 7-41, compatible) === + - variable: real_estate_taxes + geo_level: district + - variable: self_employment_income + geo_level: district + - variable: taxable_pension_income + geo_level: district + - variable: refundable_ctc + geo_level: district + - variable: unemployment_compensation + geo_level: district + + # === DISTRICT — ACA PTC === + - variable: aca_ptc + geo_level: district + - variable: tax_unit_count + geo_level: district + domain_variable: aca_ptc + + # === STATE === + - variable: person_count + geo_level: state + domain_variable: medicaid_enrolled + - variable: snap + geo_level: state + + # === NATIONAL === + - variable: child_support_expense + geo_level: national + - variable: child_support_received + geo_level: national + - variable: eitc + geo_level: national + - variable: health_insurance_premiums_without_medicare_part_b + geo_level: national + - variable: medicaid + geo_level: national + - variable: medicare_part_b_premiums + geo_level: national + - variable: other_medical_expenses + geo_level: national + - variable: over_the_counter_health_expenses + geo_level: national + - variable: social_security + geo_level: national + - variable: social_security_disability + geo_level: national + - variable: social_security_retirement + geo_level: national + - variable: spm_unit_capped_housing_subsidy + geo_level: national + - variable: spm_unit_capped_work_childcare_expenses + geo_level: national + - variable: ssi + geo_level: national + - variable: tanf + geo_level: national + - variable: rent + geo_level: national + - variable: tip_income + geo_level: national diff --git a/policyengine_us_data/calibration/target_config_full.yaml b/policyengine_us_data/calibration/target_config_full.yaml new file mode 100644 index 00000000..1e1e287d --- /dev/null +++ b/policyengine_us_data/calibration/target_config_full.yaml @@ -0,0 +1,51 @@ +# Target exclusion config for unified calibration. +# Each entry excludes targets matching (variable, geo_level). +# Derived from junkyard's 22 excluded target groups. + +exclude: + # National exclusions + - variable: alimony_expense + geo_level: national + - variable: alimony_income + geo_level: national + - variable: charitable_deduction + geo_level: national + - variable: child_support_expense + geo_level: national + - variable: child_support_received + geo_level: national + - variable: interest_deduction + geo_level: national + - variable: medical_expense_deduction + geo_level: national + - variable: net_worth + geo_level: national + - variable: person_count + geo_level: national + - variable: real_estate_taxes + geo_level: national + - variable: rent + geo_level: national + - variable: social_security_dependents + geo_level: national + - variable: social_security_survivors + geo_level: national + # District exclusions + - variable: aca_ptc + geo_level: district + - variable: eitc + geo_level: district + - variable: income_tax_before_credits + geo_level: district + - variable: medical_expense_deduction + geo_level: district + - variable: net_capital_gains + geo_level: district + - variable: rental_income + geo_level: district + - variable: tax_unit_count + geo_level: district + - variable: tax_unit_partnership_s_corp_income + geo_level: district + - variable: taxable_social_security + geo_level: district diff --git a/policyengine_us_data/calibration/unified_calibration.py b/policyengine_us_data/calibration/unified_calibration.py index 1fb7a6b3..bcfca40c 100644 --- a/policyengine_us_data/calibration/unified_calibration.py +++ b/policyengine_us_data/calibration/unified_calibration.py @@ -5,11 +5,11 @@ 1. Load CPS dataset -> get n_records 2. Clone Nx, assign random geography (census block) 3. (Optional) Source impute ACS/SIPP/SCF vars with state - 4. (Optional) PUF clone (2x) + QRF impute with state - 5. Re-randomize simple takeup variables per block - 6. Build sparse calibration matrix (clone-by-clone) - 7. L0-regularized optimization -> calibrated weights - 8. Save weights, diagnostics, run config + 4. Build sparse calibration matrix (clone-by-clone) + 5. L0-regularized optimization -> calibrated weights + 6. Save weights, diagnostics, run config + +Note: PUF cloning happens upstream in `extended_cps.py`, not here. Two presets control output size via L0 regularization: - local: L0=1e-8, ~3-4M records (for local area dataset) @@ -22,18 +22,21 @@ --output path/to/weights.npy \\ --preset local \\ --epochs 100 \\ - --puf-dataset path/to/puf_2024.h5 + --skip-source-impute """ import argparse import builtins import logging +import os import sys from pathlib import Path from typing import Optional import numpy as np +from policyengine_us_data.utils.takeup import SIMPLE_TAKEUP_VARS + logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", @@ -55,55 +58,7 @@ LAMBDA_L2 = 1e-12 LEARNING_RATE = 0.15 DEFAULT_EPOCHS = 100 -DEFAULT_N_CLONES = 10 - -SIMPLE_TAKEUP_VARS = [ - { - "variable": "takes_up_snap_if_eligible", - "entity": "spm_unit", - "rate_key": "snap", - }, - { - "variable": "takes_up_aca_if_eligible", - "entity": "tax_unit", - "rate_key": "aca", - }, - { - "variable": "takes_up_dc_ptc", - "entity": "tax_unit", - "rate_key": "dc_ptc", - }, - { - "variable": "takes_up_head_start_if_eligible", - "entity": "person", - "rate_key": "head_start", - }, - { - "variable": "takes_up_early_head_start_if_eligible", - "entity": "person", - "rate_key": "early_head_start", - }, - { - "variable": "takes_up_ssi_if_eligible", - "entity": "person", - "rate_key": "ssi", - }, - { - "variable": "would_file_taxes_voluntarily", - "entity": "tax_unit", - "rate_key": "voluntary_filing", - }, - { - "variable": "takes_up_medicaid_if_eligible", - "entity": "person", - "rate_key": "medicaid", - }, - { - "variable": "takes_up_tanf_if_eligible", - "entity": "spm_unit", - "rate_key": "tanf", - }, -] +DEFAULT_N_CLONES = 436 def rerandomize_takeup( @@ -257,23 +212,287 @@ def parse_args(argv=None): help="Skip takeup re-randomization", ) parser.add_argument( - "--puf-dataset", + "--skip-source-impute", + action="store_true", + help="Skip ACS/SIPP/SCF re-imputation with state", + ) + parser.add_argument( + "--target-config", default=None, - help="Path to PUF h5 file for QRF training", + help="Path to target exclusion YAML config", ) parser.add_argument( - "--skip-puf", + "--county-level", action="store_true", - help="Skip PUF clone + QRF imputation", + help="Iterate per-county (slow, ~3143 counties). " + "Default is state-only (~51 states), which is much " + "faster for county-invariant target variables.", ) parser.add_argument( - "--skip-source-impute", + "--build-only", action="store_true", - help="Skip ACS/SIPP/SCF re-imputation with state", + help="Build matrix + save package, skip fitting", + ) + parser.add_argument( + "--package-path", + default=None, + help="Load pre-built calibration package (skip matrix build)", + ) + parser.add_argument( + "--package-output", + default=None, + help="Where to save calibration package", + ) + parser.add_argument( + "--beta", + type=float, + default=BETA, + help=f"L0 gate temperature (default: {BETA})", + ) + parser.add_argument( + "--lambda-l2", + type=float, + default=LAMBDA_L2, + help=f"L2 regularization (default: {LAMBDA_L2})", + ) + parser.add_argument( + "--learning-rate", + type=float, + default=LEARNING_RATE, + help=f"Learning rate (default: {LEARNING_RATE})", + ) + parser.add_argument( + "--log-freq", + type=int, + default=None, + help="Epochs between per-target CSV log entries. " + "Omit to disable epoch logging.", + ) + parser.add_argument( + "--workers", + type=int, + default=1, + help="Number of parallel workers for state/county " + "precomputation (default: 1, sequential).", ) return parser.parse_args(argv) +def load_target_config(path: str) -> dict: + """Load target exclusion config from YAML. + + Args: + path: Path to YAML config file. + + Returns: + Parsed config dict with 'exclude' list. + """ + import yaml + + with open(path) as f: + config = yaml.safe_load(f) + if config is None: + config = {} + if "exclude" not in config: + config["exclude"] = [] + return config + + +def _match_rules(targets_df, rules): + """Build a boolean mask matching any of the given rules.""" + mask = np.zeros(len(targets_df), dtype=bool) + for rule in rules: + rule_mask = targets_df["variable"] == rule["variable"] + if "geo_level" in rule: + rule_mask = rule_mask & ( + targets_df["geo_level"] == rule["geo_level"] + ) + if "domain_variable" in rule: + rule_mask = rule_mask & ( + targets_df["domain_variable"] == rule["domain_variable"] + ) + mask |= rule_mask + return mask + + +def apply_target_config( + targets_df: "pd.DataFrame", + X_sparse, + target_names: list, + config: dict, +) -> tuple: + """Filter targets based on include/exclude config. + + Use ``include`` to keep only matching targets, or ``exclude`` + to drop matching targets. Both support ``variable``, + ``geo_level`` (optional), and ``domain_variable`` (optional). + If both are present, ``include`` is applied first, then + ``exclude`` removes from the included set. + + Args: + targets_df: DataFrame with target rows. + X_sparse: Sparse matrix (targets x records). + target_names: List of target name strings. + config: Config dict with 'include' and/or 'exclude' list. + + Returns: + (filtered_targets_df, filtered_X_sparse, filtered_names) + """ + include_rules = config.get("include", []) + exclude_rules = config.get("exclude", []) + + if not include_rules and not exclude_rules: + return targets_df, X_sparse, target_names + + n_before = len(targets_df) + + if include_rules: + keep_mask = _match_rules(targets_df, include_rules) + else: + keep_mask = np.ones(n_before, dtype=bool) + + if exclude_rules: + drop_mask = _match_rules(targets_df, exclude_rules) + keep_mask &= ~drop_mask + + n_dropped = n_before - keep_mask.sum() + logger.info( + "Target config: kept %d / %d targets (dropped %d)", + keep_mask.sum(), + n_before, + n_dropped, + ) + + idx = np.where(keep_mask)[0] + filtered_df = targets_df.iloc[idx].reset_index(drop=True) + filtered_X = X_sparse[idx, :] + filtered_names = [target_names[i] for i in idx] + + return filtered_df, filtered_X, filtered_names + + +def save_calibration_package( + path: str, + X_sparse, + targets_df: "pd.DataFrame", + target_names: list, + metadata: dict, + initial_weights: np.ndarray = None, + cd_geoid: np.ndarray = None, + block_geoid: np.ndarray = None, +) -> None: + """Save calibration package to pickle. + + Args: + path: Output file path. + X_sparse: Sparse matrix. + targets_df: Targets DataFrame. + target_names: Target name list. + metadata: Run metadata dict. + initial_weights: Pre-computed initial weight array. + cd_geoid: CD GEOID array from geography assignment. + block_geoid: Block GEOID array from geography assignment. + """ + import pickle + + package = { + "X_sparse": X_sparse, + "targets_df": targets_df, + "target_names": target_names, + "metadata": metadata, + "initial_weights": initial_weights, + "cd_geoid": cd_geoid, + "block_geoid": block_geoid, + } + Path(path).parent.mkdir(parents=True, exist_ok=True) + with open(path, "wb") as f: + pickle.dump(package, f, protocol=pickle.HIGHEST_PROTOCOL) + logger.info("Calibration package saved to %s", path) + + +def load_calibration_package(path: str) -> dict: + """Load calibration package from pickle. + + Args: + path: Path to package file. + + Returns: + Dict with X_sparse, targets_df, target_names, metadata. + """ + import pickle + + with open(path, "rb") as f: + package = pickle.load(f) + logger.info( + "Loaded package: %d targets, %d records", + package["X_sparse"].shape[0], + package["X_sparse"].shape[1], + ) + return package + + +def compute_initial_weights( + X_sparse, + targets_df: "pd.DataFrame", +) -> np.ndarray: + """Compute population-based initial weights from age targets. + + For each congressional district, sums person_count targets where + domain_variable == "age" to get district population, then divides + by the number of columns (households) active in that district. + + Args: + X_sparse: Sparse matrix (targets x records). + targets_df: Targets DataFrame with columns: variable, + domain_variable, geo_level, geographic_id, value. + + Returns: + Weight array of shape (n_records,). + """ + n_total = X_sparse.shape[1] + + age_mask = ( + (targets_df["variable"] == "person_count") + & (targets_df["domain_variable"] == "age") + & (targets_df["geo_level"] == "district") + ) + age_rows = targets_df[age_mask] + + if len(age_rows) == 0: + logger.warning( + "No person_count/age/district targets found; " + "falling back to uniform weights=100" + ) + return np.ones(n_total) * 100 + + initial_weights = np.ones(n_total) * 100 + cd_groups = age_rows.groupby("geographic_id") + + for cd_id, group in cd_groups: + cd_pop = group["value"].sum() + row_indices = group.index.tolist() + col_set = set() + for ri in row_indices: + row = X_sparse[ri] + col_set.update(row.indices) + n_cols = len(col_set) + if n_cols == 0: + continue + w = cd_pop / n_cols + for c in col_set: + initial_weights[c] = w + + n_unique = len(np.unique(initial_weights)) + logger.info( + "Initial weights: min=%.1f, max=%.1f, mean=%.1f, " "%d unique values", + initial_weights.min(), + initial_weights.max(), + initial_weights.mean(), + n_unique, + ) + return initial_weights + + def fit_l0_weights( X_sparse, targets: np.ndarray, @@ -281,6 +500,14 @@ def fit_l0_weights( epochs: int = DEFAULT_EPOCHS, device: str = "cpu", verbose_freq: Optional[int] = None, + beta: float = BETA, + lambda_l2: float = LAMBDA_L2, + learning_rate: float = LEARNING_RATE, + log_freq: int = None, + log_path: str = None, + target_names: list = None, + initial_weights: np.ndarray = None, + targets_df: "pd.DataFrame" = None, ) -> np.ndarray: """Fit L0-regularized calibration weights. @@ -291,6 +518,17 @@ def fit_l0_weights( epochs: Training epochs. device: Torch device. verbose_freq: Print frequency. Defaults to 10%. + beta: L0 gate temperature. + lambda_l2: L2 regularization strength. + learning_rate: Optimizer learning rate. + log_freq: Epochs between per-target CSV logs. + None disables logging. + log_path: Path for the per-target calibration log CSV. + target_names: Human-readable target names for the log. + initial_weights: Pre-computed initial weights. If None, + computed from targets_df age targets. + targets_df: Targets DataFrame, used to compute + initial_weights when not provided. Returns: Weight array of shape (n_records,). @@ -304,21 +542,30 @@ def fit_l0_weights( import torch + os.environ.setdefault( + "PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True" + ) + n_total = X_sparse.shape[1] - initial_weights = np.ones(n_total) * 100 + if initial_weights is None: + initial_weights = compute_initial_weights(X_sparse, targets_df) logger.info( "L0 calibration: %d targets, %d features, " - "lambda_l0=%.1e, epochs=%d", + "lambda_l0=%.1e, beta=%.2f, lambda_l2=%.1e, " + "lr=%.3f, epochs=%d", X_sparse.shape[0], n_total, lambda_l0, + beta, + lambda_l2, + learning_rate, epochs, ) model = SparseCalibrationWeights( n_features=n_total, - beta=BETA, + beta=beta, gamma=GAMMA, zeta=ZETA, init_keep_prob=INIT_KEEP_PROB, @@ -339,22 +586,111 @@ def _flushed_print(*args, **kwargs): builtins.print = _flushed_print - t0 = time.time() - try: - model.fit( - M=X_sparse, - y=targets, - target_groups=None, - lambda_l0=lambda_l0, - lambda_l2=LAMBDA_L2, - lr=LEARNING_RATE, - epochs=epochs, - loss_type="relative", - verbose=True, - verbose_freq=verbose_freq, + enable_logging = ( + log_freq is not None + and log_path is not None + and target_names is not None + ) + if enable_logging: + Path(log_path).parent.mkdir(parents=True, exist_ok=True) + with open(log_path, "w") as f: + f.write( + "target_name,estimate,target,epoch," + "error,rel_error,abs_error,rel_abs_error,loss\n" + ) + logger.info( + "Epoch logging enabled: freq=%d, path=%s", + log_freq, + log_path, ) - finally: - builtins.print = _builtin_print + + t0 = time.time() + if enable_logging: + epochs_done = 0 + while epochs_done < epochs: + chunk = min(log_freq, epochs - epochs_done) + model.fit( + M=X_sparse, + y=targets, + target_groups=None, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + lr=learning_rate, + epochs=chunk, + loss_type="relative", + verbose=False, + ) + + epochs_done += chunk + + with torch.no_grad(): + y_pred = model.predict(X_sparse).cpu().numpy() + weights_snap = ( + model.get_weights(deterministic=True).cpu().numpy() + ) + + nz = (weights_snap > 0).sum() + sparsity = (1 - nz / n_total) * 100 + + rel_errs = np.where( + np.abs(targets) > 0, + (y_pred - targets) / np.abs(targets), + 0.0, + ) + mean_err = np.mean(np.abs(rel_errs)) + max_err = np.max(np.abs(rel_errs)) + total_loss = np.sum(rel_errs**2) + + print( + f"Epoch {epochs_done:4d}: " + f"mean_error={mean_err:.4%}, " + f"max_error={max_err:.1%}, " + f"total_loss={total_loss:.3f}, " + f"active={nz}/{n_total} " + f"({sparsity:.1f}% sparse)", + flush=True, + ) + + with open(log_path, "a") as f: + for i in range(len(targets)): + est = y_pred[i] + tgt = targets[i] + err = est - tgt + rel_err = err / tgt if tgt != 0 else 0 + abs_err = abs(err) + rel_abs = abs(rel_err) + loss = rel_err**2 + f.write( + f'"{target_names[i]}",' + f"{est},{tgt},{epochs_done}," + f"{err},{rel_err},{abs_err}," + f"{rel_abs},{loss}\n" + ) + + logger.info( + "Logged %d targets at epoch %d", + len(targets), + epochs_done, + ) + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + else: + try: + model.fit( + M=X_sparse, + y=targets, + target_groups=None, + lambda_l0=lambda_l0, + lambda_l2=lambda_l2, + lr=learning_rate, + epochs=epochs, + loss_type="relative", + verbose=True, + verbose_freq=verbose_freq, + ) + finally: + builtins.print = _builtin_print elapsed = time.time() - t0 logger.info( @@ -376,6 +712,119 @@ def _flushed_print(*args, **kwargs): return weights +def convert_weights_to_stacked_format( + weights: np.ndarray, + cd_geoid: np.ndarray, + base_n_records: int, + cds_ordered: list, +) -> np.ndarray: + """Convert column-ordered weights to (n_cds, n_records) stacked format. + + The L0 calibration produces one weight per column, where columns + are ordered by clone (column i -> clone i // n_records, record + i % n_records) with random CD assignments. This function + aggregates weights across clones into the (n_cds, n_records) + layout expected by stacked_dataset_builder. + + Args: + weights: Raw weight vector from L0 fitting, length + n_clones * base_n_records. + cd_geoid: CD GEOID per column from geography assignment. + base_n_records: Number of base households (before cloning). + cds_ordered: Ordered list of CD GEOIDs defining row order. + + Returns: + Flat array of length n_cds * base_n_records that reshapes + to (n_cds, base_n_records). + """ + n_total = len(weights) + n_cds = len(cds_ordered) + + cd_to_idx = {cd: idx for idx, cd in enumerate(cds_ordered)} + record_indices = np.arange(n_total) % base_n_records + cd_row_indices = np.array([cd_to_idx[cd] for cd in cd_geoid]) + flat_indices = cd_row_indices * base_n_records + record_indices + + W = np.zeros(n_cds * base_n_records, dtype=np.float64) + np.add.at(W, flat_indices, weights) + + assert np.isclose( + W.sum(), weights.sum() + ), f"Weight sum mismatch: {W.sum()} vs {weights.sum()}" + logger.info( + "Converted weights to stacked format: " + "(%d, %d) = %d elements, sum=%.1f", + n_cds, + base_n_records, + len(W), + W.sum(), + ) + return W + + +def convert_blocks_to_stacked_format( + block_geoid: np.ndarray, + cd_geoid: np.ndarray, + base_n_records: int, + cds_ordered: list, +) -> np.ndarray: + """Convert column-ordered block GEOIDs to stacked format. + + Parallel to convert_weights_to_stacked_format. For each + (CD, record) slot, stores the block GEOID from the first + clone assigned there. Empty string for unfilled slots + (records with no clone in that CD). + + Args: + block_geoid: Block GEOID per column from geography + assignment. Length n_clones * base_n_records. + cd_geoid: CD GEOID per column from geography + assignment. + base_n_records: Number of base households. + cds_ordered: Ordered list of CD GEOIDs defining + row order. + + Returns: + Array of dtype U15, length n_cds * base_n_records, + reshapeable to (n_cds, base_n_records). + """ + n_total = len(block_geoid) + n_cds = len(cds_ordered) + + cd_to_idx = {cd: idx for idx, cd in enumerate(cds_ordered)} + record_indices = np.arange(n_total) % base_n_records + cd_row_indices = np.array([cd_to_idx[cd] for cd in cd_geoid]) + flat_indices = cd_row_indices * base_n_records + record_indices + + B = np.full(n_cds * base_n_records, "", dtype="U15") + n_collisions = 0 + for i in range(n_total): + fi = flat_indices[i] + if B[fi] == "": + B[fi] = block_geoid[i] + else: + n_collisions += 1 + + if n_collisions > 0: + logger.warning( + "Block collisions: %d slots had multiple clones " + "with different blocks.", + n_collisions, + ) + + n_filled = np.count_nonzero(B != "") + logger.info( + "Converted blocks to stacked format: " + "(%d, %d) = %d slots, %d filled (%.1f%%)", + n_cds, + base_n_records, + len(B), + n_filled, + n_filled / len(B) * 100, + ) + return B + + def compute_diagnostics( weights: np.ndarray, X_sparse, @@ -405,88 +854,6 @@ def compute_diagnostics( ) -def _build_puf_cloned_dataset( - dataset_path: str, - puf_dataset_path: str, - state_fips: np.ndarray, - time_period: int = 2024, - skip_qrf: bool = False, - skip_source_impute: bool = False, -) -> str: - """Build a PUF-cloned dataset from raw CPS. - - Loads the CPS, optionally runs source imputations - (ACS/SIPP/SCF), then PUF clone + QRF. - - Args: - dataset_path: Path to raw CPS h5 file. - puf_dataset_path: Path to PUF h5 file. - state_fips: State FIPS per household (base records). - time_period: Tax year. - skip_qrf: Skip QRF imputation. - skip_source_impute: Skip ACS/SIPP/SCF imputations. - - Returns: - Path to the PUF-cloned h5 file. - """ - import h5py - - from policyengine_us import Microsimulation - - from policyengine_us_data.calibration.puf_impute import ( - puf_clone_dataset, - ) - - logger.info("Building PUF-cloned dataset from %s", dataset_path) - - sim = Microsimulation(dataset=dataset_path) - data = sim.dataset.load_dataset() - - data_dict = {} - for var in data: - if isinstance(data[var], dict): - vals = list(data[var].values()) - data_dict[var] = {time_period: vals[0]} - else: - data_dict[var] = {time_period: np.array(data[var])} - - if not skip_source_impute: - from policyengine_us_data.calibration.source_impute import ( - impute_source_variables, - ) - - data_dict = impute_source_variables( - data=data_dict, - state_fips=state_fips, - time_period=time_period, - dataset_path=dataset_path, - ) - - puf_dataset = puf_dataset_path if not skip_qrf else None - - new_data = puf_clone_dataset( - data=data_dict, - state_fips=state_fips, - time_period=time_period, - puf_dataset=puf_dataset, - skip_qrf=skip_qrf, - dataset_path=dataset_path, - ) - - output_path = str( - Path(dataset_path).parent / f"puf_cloned_{Path(dataset_path).stem}.h5" - ) - - with h5py.File(output_path, "w") as f: - for var, time_dict in new_data.items(): - for tp, values in time_dict.items(): - f.create_dataset(f"{var}/{tp}", data=values) - - del sim - logger.info("PUF-cloned dataset saved to %s", output_path) - return output_path - - def run_calibration( dataset_path: str, db_path: str, @@ -498,9 +865,18 @@ def run_calibration( domain_variables: list = None, hierarchical_domains: list = None, skip_takeup_rerandomize: bool = False, - puf_dataset_path: str = None, - skip_puf: bool = False, skip_source_impute: bool = False, + skip_county: bool = True, + target_config: dict = None, + build_only: bool = False, + package_path: str = None, + package_output_path: str = None, + beta: float = BETA, + lambda_l2: float = LAMBDA_L2, + learning_rate: float = LEARNING_RATE, + log_freq: int = None, + log_path: str = None, + workers: int = 1, ): """Run unified calibration pipeline. @@ -516,32 +892,95 @@ def run_calibration( hierarchical_domains: Domains for hierarchical uprating + CD reconciliation. skip_takeup_rerandomize: Skip takeup step. - puf_dataset_path: Path to PUF h5 for QRF training. - skip_puf: Skip PUF clone step. skip_source_impute: Skip ACS/SIPP/SCF imputations. + target_config: Parsed target config dict. + build_only: If True, save package and skip fitting. + package_path: Load pre-built package (skip build). + package_output_path: Where to save calibration package. + beta: L0 gate temperature. + lambda_l2: L2 regularization strength. + learning_rate: Optimizer learning rate. + log_freq: Epochs between per-target CSV logs. + log_path: Path for per-target calibration log CSV. Returns: - (weights, targets_df, X_sparse, target_names) + (weights, targets_df, X_sparse, target_names, geography_info) + weights is None when build_only=True. + geography_info is a dict with cd_geoid and base_n_records. """ import time + t0 = time.time() + + # Early exit: load pre-built package + if package_path is not None: + package = load_calibration_package(package_path) + targets_df = package["targets_df"] + X_sparse = package["X_sparse"] + target_names = package["target_names"] + + if target_config: + targets_df, X_sparse, target_names = apply_target_config( + targets_df, X_sparse, target_names, target_config + ) + + initial_weights = package.get("initial_weights") + targets = targets_df["value"].values + weights = fit_l0_weights( + X_sparse=X_sparse, + targets=targets, + lambda_l0=lambda_l0, + epochs=epochs, + device=device, + beta=beta, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + log_path=log_path, + target_names=target_names, + initial_weights=initial_weights, + targets_df=targets_df, + ) + logger.info( + "Total pipeline (from package): %.1f min", + (time.time() - t0) / 60, + ) + geography_info = { + "cd_geoid": package.get("cd_geoid"), + "block_geoid": package.get("block_geoid"), + "base_n_records": package["metadata"].get("base_n_records"), + } + return ( + weights, + targets_df, + X_sparse, + target_names, + geography_info, + ) + from policyengine_us import Microsimulation from policyengine_us_data.calibration.clone_and_assign import ( assign_random_geography, - double_geography_for_puf, ) from policyengine_us_data.calibration.unified_matrix_builder import ( UnifiedMatrixBuilder, ) - t0 = time.time() - - # Step 1: Load dataset + # Step 1: Load dataset and detect time period logger.info("Loading dataset from %s", dataset_path) sim = Microsimulation(dataset=dataset_path) n_records = len(sim.calculate("household_id", map_to="household").values) - logger.info("Loaded %d households", n_records) + raw_keys = sim.dataset.load_dataset()["household_id"] + if isinstance(raw_keys, dict): + time_period = int(next(iter(raw_keys))) + else: + time_period = 2024 + logger.info( + "Loaded %d households, time_period=%d", + n_records, + time_period, + ) # Step 2: Clone and assign geography logger.info( @@ -556,46 +995,28 @@ def run_calibration( seed=seed, ) - # Step 3: Source impute + PUF clone (if requested) + # Step 3: Source imputation (if requested) dataset_for_matrix = dataset_path - if not skip_puf and puf_dataset_path is not None: - base_states = geography.state_fips[:n_records] - - puf_cloned_path = _build_puf_cloned_dataset( - dataset_path=dataset_path, - puf_dataset_path=puf_dataset_path, - state_fips=base_states, - time_period=2024, - skip_qrf=False, - skip_source_impute=skip_source_impute, - ) - - geography = double_geography_for_puf(geography) - dataset_for_matrix = puf_cloned_path - n_records = n_records * 2 - - # Reload sim from PUF-cloned dataset - del sim - sim = Microsimulation(dataset=puf_cloned_path) - - logger.info( - "After PUF clone: %d records x %d clones = %d", - n_records, - n_clones, - n_records * n_clones, - ) - elif not skip_source_impute: + if not skip_source_impute: # Run source imputations without PUF cloning import h5py base_states = geography.state_fips[:n_records] - source_sim = Microsimulation(dataset=dataset_path) - raw_data = source_sim.dataset.load_dataset() + raw_data = sim.dataset.load_dataset() data_dict = {} for var in raw_data: - data_dict[var] = {2024: raw_data[var][...]} - del source_sim + val = raw_data[var] + if isinstance(val, dict): + # h5py returns string keys ("2024"); normalize + # to int so source_impute lookups work. + # Some keys like "ETERNITY" are non-numeric — keep + # them as strings. + data_dict[var] = { + int(k) if k.isdigit() else k: v for k, v in val.items() + } + else: + data_dict[var] = {time_period: val[...]} from policyengine_us_data.calibration.source_impute import ( impute_source_variables, @@ -604,7 +1025,7 @@ def run_calibration( data_dict = impute_source_variables( data=data_dict, state_fips=base_states, - time_period=2024, + time_period=time_period, dataset_path=dataset_path, ) @@ -625,17 +1046,7 @@ def run_calibration( source_path, ) - # Step 4: Build sim_modifier for takeup rerandomization sim_modifier = None - if not skip_takeup_rerandomize: - time_period = 2024 - - def sim_modifier(s, clone_idx): - col_start = clone_idx * n_records - col_end = col_start + n_records - blocks = geography.block_geoid[col_start:col_end] - states = geography.state_fips[col_start:col_end] - rerandomize_takeup(s, blocks, states, time_period) # Step 5: Build target filter target_filter = {} @@ -643,11 +1054,12 @@ def sim_modifier(s, clone_idx): target_filter["domain_variables"] = domain_variables # Step 6: Build sparse calibration matrix + do_rerandomize = not skip_takeup_rerandomize t_matrix = time.time() db_uri = f"sqlite:///{db_path}" builder = UnifiedMatrixBuilder( db_uri=db_uri, - time_period=2024, + time_period=time_period, dataset_path=dataset_for_matrix, ) targets_df, X_sparse, target_names = builder.build_matrix( @@ -656,6 +1068,9 @@ def sim_modifier(s, clone_idx): target_filter=target_filter, hierarchical_domains=hierarchical_domains, sim_modifier=sim_modifier, + rerandomize_takeup=do_rerandomize, + county_level=not skip_county, + workers=workers, ) builder.print_uprating_summary(targets_df) @@ -669,6 +1084,70 @@ def sim_modifier(s, clone_idx): X_sparse.nnz, ) + # Step 6b: Save FULL (unfiltered) calibration package. + # Target config is applied at fit time, so the package can be + # reused with different configs without rebuilding. + import datetime + + metadata = { + "dataset_path": dataset_path, + "db_path": db_path, + "n_clones": n_clones, + "n_records": X_sparse.shape[1], + "base_n_records": n_records, + "seed": seed, + "created_at": datetime.datetime.now().isoformat(), + } + + if package_output_path: + full_initial_weights = compute_initial_weights(X_sparse, targets_df) + save_calibration_package( + package_output_path, + X_sparse, + targets_df, + target_names, + metadata, + initial_weights=full_initial_weights, + cd_geoid=geography.cd_geoid, + block_geoid=geography.block_geoid, + ) + + # Step 6c: Apply target config filtering (for fit or validation) + if target_config: + targets_df, X_sparse, target_names = apply_target_config( + targets_df, X_sparse, target_names, target_config + ) + + initial_weights = compute_initial_weights(X_sparse, targets_df) + + if build_only: + from policyengine_us_data.calibration.validate_package import ( + validate_package, + format_report, + ) + + package = { + "X_sparse": X_sparse, + "targets_df": targets_df, + "target_names": target_names, + "metadata": metadata, + "initial_weights": initial_weights, + } + result = validate_package(package) + print(format_report(result)) + geography_info = { + "cd_geoid": geography.cd_geoid, + "block_geoid": geography.block_geoid, + "base_n_records": n_records, + } + return ( + None, + targets_df, + X_sparse, + target_names, + geography_info, + ) + # Step 7: L0 calibration targets = targets_df["value"].values @@ -686,13 +1165,32 @@ def sim_modifier(s, clone_idx): lambda_l0=lambda_l0, epochs=epochs, device=device, + beta=beta, + lambda_l2=lambda_l2, + learning_rate=learning_rate, + log_freq=log_freq, + log_path=log_path, + target_names=target_names, + initial_weights=initial_weights, + targets_df=targets_df, ) logger.info( "Total pipeline: %.1f min", (time.time() - t0) / 60, ) - return weights, targets_df, X_sparse, target_names + geography_info = { + "cd_geoid": geography.cd_geoid, + "block_geoid": geography.block_geoid, + "base_n_records": n_records, + } + return ( + weights, + targets_df, + X_sparse, + target_names, + geography_info, + ) def main(argv=None): @@ -710,6 +1208,7 @@ def main(argv=None): pass args = parse_args(argv) + logger.info("CLI args: %s", vars(args)) from policyengine_us_data.storage import STORAGE_FOLDER @@ -744,7 +1243,27 @@ def main(argv=None): t_start = time.time() - weights, targets_df, X_sparse, target_names = run_calibration( + target_config = None + if args.target_config: + target_config = load_target_config(args.target_config) + + package_output_path = args.package_output + if args.build_only and not package_output_path: + package_output_path = str( + STORAGE_FOLDER / "calibration" / "calibration_package.pkl" + ) + + output_dir = Path(output_path).parent + cal_log_path = None + if args.log_freq is not None: + cal_log_path = str(output_dir / "calibration_log.csv") + ( + weights, + targets_df, + X_sparse, + target_names, + geography_info, + ) = run_calibration( dataset_path=dataset_path, db_path=db_path, n_clones=args.n_clones, @@ -755,17 +1274,25 @@ def main(argv=None): domain_variables=domain_variables, hierarchical_domains=hierarchical_domains, skip_takeup_rerandomize=args.skip_takeup_rerandomize, - puf_dataset_path=args.puf_dataset, - skip_puf=args.skip_puf, - skip_source_impute=args.skip_source_impute, + skip_source_impute=getattr(args, "skip_source_impute", False), + skip_county=not args.county_level, + target_config=target_config, + build_only=args.build_only, + package_path=args.package_path, + package_output_path=package_output_path, + beta=args.beta, + lambda_l2=args.lambda_l2, + learning_rate=args.learning_rate, + log_freq=args.log_freq, + log_path=cal_log_path, + workers=args.workers, ) - # Save weights - np.save(output_path, weights) - logger.info("Weights saved to %s", output_path) - print(f"OUTPUT_PATH:{output_path}") + if weights is None: + logger.info("Build-only complete. Package saved.") + return - # Save diagnostics + # Diagnostics (raw weights match X_sparse column layout) output_dir = Path(output_path).parent diag_df = compute_diagnostics(weights, X_sparse, targets_df, target_names) diag_path = output_dir / "unified_diagnostics.csv" @@ -784,25 +1311,72 @@ def main(argv=None): (err_pct < 25).mean() * 100, ) + # Convert to stacked format for stacked_dataset_builder + cd_geoid = geography_info.get("cd_geoid") + base_n_records = geography_info.get("base_n_records") + + if cd_geoid is not None and base_n_records is not None: + cds_ordered = sorted(set(cd_geoid)) + stacked_weights = convert_weights_to_stacked_format( + weights=weights, + cd_geoid=cd_geoid, + base_n_records=base_n_records, + cds_ordered=cds_ordered, + ) + else: + logger.warning("No geography info available; saving raw weights") + stacked_weights = weights + + # Save stacked blocks alongside weights + block_geoid = geography_info.get("block_geoid") + if ( + block_geoid is not None + and cd_geoid is not None + and base_n_records is not None + ): + blocks_stacked = convert_blocks_to_stacked_format( + block_geoid=block_geoid, + cd_geoid=cd_geoid, + base_n_records=base_n_records, + cds_ordered=cds_ordered, + ) + blocks_path = output_dir / "stacked_blocks.npy" + np.save(str(blocks_path), blocks_stacked) + logger.info("Stacked blocks saved to %s", blocks_path) + + # Save weights + Path(output_path).parent.mkdir(parents=True, exist_ok=True) + np.save(output_path, stacked_weights) + logger.info("Weights saved to %s", output_path) + print(f"OUTPUT_PATH:{output_path}") + # Save run config t_end = time.time() + weight_format = ( + "stacked" + if cd_geoid is not None and base_n_records is not None + else "raw" + ) run_config = { "dataset": dataset_path, "db_path": db_path, - "puf_dataset": args.puf_dataset, - "skip_puf": args.skip_puf, "skip_source_impute": args.skip_source_impute, "n_clones": args.n_clones, "lambda_l0": lambda_l0, + "beta": args.beta, + "lambda_l2": args.lambda_l2, + "learning_rate": args.learning_rate, "epochs": args.epochs, "device": args.device, "seed": args.seed, "domain_variables": domain_variables, "hierarchical_domains": hierarchical_domains, + "target_config": args.target_config, "n_targets": len(targets_df), "n_records": X_sparse.shape[1], - "weight_sum": float(weights.sum()), - "weight_nonzero": int((weights > 0).sum()), + "weight_format": weight_format, + "weight_sum": float(stacked_weights.sum()), + "weight_nonzero": int((stacked_weights > 0).sum()), "mean_error_pct": float(err_pct.mean()), "elapsed_seconds": round(t_end - t_start, 1), } @@ -811,6 +1385,8 @@ def main(argv=None): json.dump(run_config, f, indent=2) logger.info("Config saved to %s", config_path) print(f"LOG_PATH:{diag_path}") + if cal_log_path: + print(f"CAL_LOG_PATH:{cal_log_path}") if __name__ == "__main__": diff --git a/policyengine_us_data/calibration/unified_matrix_builder.py b/policyengine_us_data/calibration/unified_matrix_builder.py index ac31c34e..b145b59e 100644 --- a/policyengine_us_data/calibration/unified_matrix_builder.py +++ b/policyengine_us_data/calibration/unified_matrix_builder.py @@ -26,6 +26,9 @@ apply_op, get_geo_level, ) +from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + get_county_enum_index_from_fips, +) logger = logging.getLogger(__name__) @@ -35,6 +38,673 @@ "congressional_district_geoid", } +COUNTY_DEPENDENT_VARS = { + "aca_ptc", +} + + +def _compute_single_state( + dataset_path: str, + time_period: int, + state: int, + n_hh: int, + target_vars: list, + constraint_vars: list, + rerandomize_takeup: bool, + affected_targets: dict, +): + """Compute household/person/entity values for one state. + + Top-level function (not a method) so it is picklable for + ``ProcessPoolExecutor``. + + Args: + dataset_path: Path to the base CPS h5 file. + time_period: Tax year for simulation. + state: State FIPS code. + n_hh: Number of household records. + target_vars: Target variable names (list for determinism). + constraint_vars: Constraint variable names (list). + rerandomize_takeup: Force takeup=True if True. + affected_targets: Takeup-affected target info dict. + + Returns: + (state_fips, {"hh": {...}, "person": {...}, "entity": {...}}) + """ + from policyengine_us import Microsimulation + from policyengine_us_data.utils.takeup import SIMPLE_TAKEUP_VARS + from policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import ( + get_calculated_variables, + ) + + state_sim = Microsimulation(dataset=dataset_path) + + if rerandomize_takeup: + for spec in SIMPLE_TAKEUP_VARS: + entity = spec["entity"] + n_ent = len( + state_sim.calculate(f"{entity}_id", map_to=entity).values + ) + state_sim.set_input( + spec["variable"], + time_period, + np.ones(n_ent, dtype=bool), + ) + + state_sim.set_input( + "state_fips", + time_period, + np.full(n_hh, state, dtype=np.int32), + ) + for var in get_calculated_variables(state_sim): + state_sim.delete_arrays(var) + + hh = {} + for var in target_vars: + if var.endswith("_count"): + continue + try: + hh[var] = state_sim.calculate( + var, + time_period, + map_to="household", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate '%s' for state %d: %s", + var, + state, + exc, + ) + + person = {} + for var in constraint_vars: + try: + person[var] = state_sim.calculate( + var, + time_period, + map_to="person", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate constraint '%s' " "for state %d: %s", + var, + state, + exc, + ) + + entity_vals = {} + if rerandomize_takeup: + for tvar, info in affected_targets.items(): + entity_level = info["entity"] + try: + entity_vals[tvar] = state_sim.calculate( + tvar, + time_period, + map_to=entity_level, + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate entity-level " + "'%s' (map_to=%s) for state %d: %s", + tvar, + entity_level, + state, + exc, + ) + + return (state, {"hh": hh, "person": person, "entity": entity_vals}) + + +def _compute_single_state_group_counties( + dataset_path: str, + time_period: int, + state_fips: int, + counties: list, + n_hh: int, + county_dep_targets: list, + rerandomize_takeup: bool, + affected_targets: dict, +): + """Compute county-dependent values for all counties in one state. + + Top-level function (not a method) so it is picklable for + ``ProcessPoolExecutor``. Creates one ``Microsimulation`` per + state and reuses it across counties within that state. + + Args: + dataset_path: Path to the base CPS h5 file. + time_period: Tax year for simulation. + state_fips: State FIPS code for this group. + counties: List of county FIPS strings in this state. + n_hh: Number of household records. + county_dep_targets: County-dependent target var names. + rerandomize_takeup: Force takeup=True if True. + affected_targets: Takeup-affected target info dict. + + Returns: + list of (county_fips_str, {"hh": {...}, "entity": {...}}) + """ + from policyengine_us import Microsimulation + from policyengine_us_data.utils.takeup import SIMPLE_TAKEUP_VARS + from policyengine_us_data.datasets.cps.local_area_calibration.calibration_utils import ( + get_calculated_variables, + ) + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + get_county_enum_index_from_fips, + ) + + state_sim = Microsimulation(dataset=dataset_path) + + if rerandomize_takeup: + for spec in SIMPLE_TAKEUP_VARS: + entity = spec["entity"] + n_ent = len( + state_sim.calculate(f"{entity}_id", map_to=entity).values + ) + state_sim.set_input( + spec["variable"], + time_period, + np.ones(n_ent, dtype=bool), + ) + + state_sim.set_input( + "state_fips", + time_period, + np.full(n_hh, state_fips, dtype=np.int32), + ) + + results = [] + for county_fips in counties: + county_idx = get_county_enum_index_from_fips(county_fips) + state_sim.set_input( + "county", + time_period, + np.full(n_hh, county_idx, dtype=np.int32), + ) + for var in get_calculated_variables(state_sim): + if var != "county": + state_sim.delete_arrays(var) + + hh = {} + for var in county_dep_targets: + if var.endswith("_count"): + continue + try: + hh[var] = state_sim.calculate( + var, + time_period, + map_to="household", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate '%s' for " "county %s: %s", + var, + county_fips, + exc, + ) + + entity_vals = {} + if rerandomize_takeup: + for tvar, info in affected_targets.items(): + entity_level = info["entity"] + try: + entity_vals[tvar] = state_sim.calculate( + tvar, + time_period, + map_to=entity_level, + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate entity-level " + "'%s' for county %s: %s", + tvar, + county_fips, + exc, + ) + + results.append((county_fips, {"hh": hh, "entity": entity_vals})) + + return results + + +# --------------------------------------------------------------- +# Clone-loop parallelisation helpers (module-level for pickling) +# --------------------------------------------------------------- + +_CLONE_SHARED: dict = {} + + +def _init_clone_worker(shared_data: dict) -> None: + """Initialise worker process with shared read-only data. + + Called once per worker at ``ProcessPoolExecutor`` startup so the + ~50-200 MB payload is pickled *per worker* (not per clone). + """ + _CLONE_SHARED.update(shared_data) + + +def _assemble_clone_values_standalone( + state_values: dict, + clone_states: np.ndarray, + person_hh_indices: np.ndarray, + target_vars: set, + constraint_vars: set, + county_values: dict = None, + clone_counties: np.ndarray = None, + county_dependent_vars: set = None, +) -> tuple: + """Standalone clone-value assembly (no ``self``). + + Identical logic to + ``UnifiedMatrixBuilder._assemble_clone_values`` but usable + from a worker process. + """ + n_records = len(clone_states) + n_persons = len(person_hh_indices) + person_states = clone_states[person_hh_indices] + unique_clone_states = np.unique(clone_states) + cdv = county_dependent_vars or set() + + state_masks = {int(s): clone_states == s for s in unique_clone_states} + unique_person_states = np.unique(person_states) + person_state_masks = { + int(s): person_states == s for s in unique_person_states + } + county_masks = {} + unique_counties = None + if clone_counties is not None and county_values: + unique_counties = np.unique(clone_counties) + county_masks = {c: clone_counties == c for c in unique_counties} + + hh_vars: dict = {} + for var in target_vars: + if var.endswith("_count"): + continue + if var in cdv and county_values and clone_counties is not None: + first_county = unique_counties[0] + if var not in county_values.get(first_county, {}).get("hh", {}): + continue + arr = np.empty(n_records, dtype=np.float32) + for county in unique_counties: + mask = county_masks[county] + county_hh = county_values.get(county, {}).get("hh", {}) + if var in county_hh: + arr[mask] = county_hh[var][mask] + else: + st = int(county[:2]) + arr[mask] = state_values[st]["hh"][var][mask] + hh_vars[var] = arr + else: + if var not in state_values[unique_clone_states[0]]["hh"]: + continue + arr = np.empty(n_records, dtype=np.float32) + for state in unique_clone_states: + mask = state_masks[int(state)] + arr[mask] = state_values[int(state)]["hh"][var][mask] + hh_vars[var] = arr + + person_vars: dict = {} + for var in constraint_vars: + if var not in state_values[unique_clone_states[0]]["person"]: + continue + arr = np.empty(n_persons, dtype=np.float32) + for state in unique_person_states: + mask = person_state_masks[int(state)] + arr[mask] = state_values[int(state)]["person"][var][mask] + person_vars[var] = arr + + return hh_vars, person_vars + + +def _evaluate_constraints_standalone( + constraints, + person_vars: dict, + entity_rel: pd.DataFrame, + household_ids: np.ndarray, + n_households: int, +) -> np.ndarray: + """Standalone constraint evaluation (no ``self``). + + Same logic as + ``UnifiedMatrixBuilder._evaluate_constraints_from_values``. + """ + if not constraints: + return np.ones(n_households, dtype=bool) + + n_persons = len(entity_rel) + person_mask = np.ones(n_persons, dtype=bool) + + for c in constraints: + var = c["variable"] + if var not in person_vars: + logger.warning( + "Constraint var '%s' not in " "precomputed person_vars", + var, + ) + return np.zeros(n_households, dtype=bool) + vals = person_vars[var] + person_mask &= apply_op(vals, c["operation"], c["value"]) + + df = entity_rel.copy() + df["satisfies"] = person_mask + hh_mask = df.groupby("household_id")["satisfies"].any() + return np.array([hh_mask.get(hid, False) for hid in household_ids]) + + +def _calculate_target_values_standalone( + target_variable: str, + non_geo_constraints: list, + n_households: int, + hh_vars: dict, + person_vars: dict, + entity_rel: pd.DataFrame, + household_ids: np.ndarray, + variable_entity_map: dict, +) -> np.ndarray: + """Standalone target-value calculation (no ``self``). + + Same logic as + ``UnifiedMatrixBuilder._calculate_target_values_from_values`` + but uses ``variable_entity_map`` instead of + ``tax_benefit_system``. + """ + is_count = target_variable.endswith("_count") + + if not is_count: + mask = _evaluate_constraints_standalone( + non_geo_constraints, + person_vars, + entity_rel, + household_ids, + n_households, + ) + vals = hh_vars.get(target_variable) + if vals is None: + return np.zeros(n_households, dtype=np.float32) + return (vals * mask).astype(np.float32) + + # Count target: entity-aware counting + n_persons = len(entity_rel) + person_mask = np.ones(n_persons, dtype=bool) + + for c in non_geo_constraints: + var = c["variable"] + if var not in person_vars: + return np.zeros(n_households, dtype=np.float32) + cv = person_vars[var] + person_mask &= apply_op(cv, c["operation"], c["value"]) + + target_entity = variable_entity_map.get(target_variable) + if target_entity is None: + return np.zeros(n_households, dtype=np.float32) + + if target_entity == "household": + if non_geo_constraints: + mask = _evaluate_constraints_standalone( + non_geo_constraints, + person_vars, + entity_rel, + household_ids, + n_households, + ) + return mask.astype(np.float32) + return np.ones(n_households, dtype=np.float32) + + if target_entity == "person": + er = entity_rel.copy() + er["satisfies"] = person_mask + filtered = er[er["satisfies"]] + counts = filtered.groupby("household_id")["person_id"].nunique() + else: + eid_col = f"{target_entity}_id" + er = entity_rel.copy() + er["satisfies"] = person_mask + entity_ok = er.groupby(eid_col)["satisfies"].any() + unique = er[["household_id", eid_col]].drop_duplicates() + unique["entity_ok"] = unique[eid_col].map(entity_ok) + filtered = unique[unique["entity_ok"]] + counts = filtered.groupby("household_id")[eid_col].nunique() + + return np.array( + [counts.get(hid, 0) for hid in household_ids], + dtype=np.float32, + ) + + +def _process_single_clone( + clone_idx: int, + col_start: int, + col_end: int, + cache_path: str, +) -> tuple: + """Process one clone in a worker process. + + Reads shared read-only data from ``_CLONE_SHARED`` + (populated by ``_init_clone_worker``). Writes COO + entries as a compressed ``.npz`` file to *cache_path*. + + Args: + clone_idx: Zero-based clone index. + col_start: First column index for this clone. + col_end: One-past-last column index. + cache_path: File path for output ``.npz``. + + Returns: + (clone_idx, n_nonzero) tuple. + """ + sd = _CLONE_SHARED + + # Unpack shared data + geo_states = sd["geography_state_fips"] + geo_counties = sd["geography_county_fips"] + geo_blocks = sd["geography_block_geoid"] + state_values = sd["state_values"] + county_values = sd["county_values"] + person_hh_indices = sd["person_hh_indices"] + unique_variables = sd["unique_variables"] + unique_constraint_vars = sd["unique_constraint_vars"] + county_dep_targets = sd["county_dep_targets"] + target_variables = sd["target_variables"] + target_geo_info = sd["target_geo_info"] + non_geo_constraints_list = sd["non_geo_constraints_list"] + n_records = sd["n_records"] + n_total = sd["n_total"] + n_targets = sd["n_targets"] + state_to_cols = sd["state_to_cols"] + cd_to_cols = sd["cd_to_cols"] + entity_rel = sd["entity_rel"] + household_ids = sd["household_ids"] + variable_entity_map = sd["variable_entity_map"] + do_takeup = sd["rerandomize_takeup"] + affected_target_info = sd["affected_target_info"] + entity_hh_idx_map = sd.get("entity_hh_idx_map", {}) + entity_to_person_idx = sd.get("entity_to_person_idx", {}) + precomputed_rates = sd.get("precomputed_rates", {}) + + # Slice geography for this clone + clone_states = geo_states[col_start:col_end] + clone_counties = geo_counties[col_start:col_end] + + # Assemble hh/person values from precomputed state/county + hh_vars, person_vars = _assemble_clone_values_standalone( + state_values, + clone_states, + person_hh_indices, + unique_variables, + unique_constraint_vars, + county_values=county_values, + clone_counties=clone_counties, + county_dependent_vars=county_dep_targets, + ) + + # Takeup re-randomisation + if do_takeup and affected_target_info: + from policyengine_us_data.utils.takeup import ( + _resolve_rate, + ) + from policyengine_us_data.utils.randomness import ( + seeded_rng, + ) + + clone_blocks = geo_blocks[col_start:col_end] + + for tvar, info in affected_target_info.items(): + if tvar.endswith("_count"): + continue + entity_level = info["entity"] + takeup_var = info["takeup_var"] + ent_hh = entity_hh_idx_map[entity_level] + n_ent = len(ent_hh) + ent_states = clone_states[ent_hh] + + ent_eligible = np.zeros(n_ent, dtype=np.float32) + if tvar in county_dep_targets and county_values: + ent_counties = clone_counties[ent_hh] + for cfips in np.unique(ent_counties): + m = ent_counties == cfips + cv = county_values.get(cfips, {}).get("entity", {}) + if tvar in cv: + ent_eligible[m] = cv[tvar][m] + else: + st = int(cfips[:2]) + sv = state_values[st]["entity"] + if tvar in sv: + ent_eligible[m] = sv[tvar][m] + else: + for st in np.unique(ent_states): + m = ent_states == st + sv = state_values[int(st)]["entity"] + if tvar in sv: + ent_eligible[m] = sv[tvar][m] + + ent_blocks = clone_blocks[ent_hh] + ent_hh_ids = household_ids[ent_hh] + + ent_takeup = np.zeros(n_ent, dtype=bool) + rate_key = info["rate_key"] + rate_or_dict = precomputed_rates[rate_key] + for blk in np.unique(ent_blocks): + bm = ent_blocks == blk + sf = int(blk[:2]) + rate = _resolve_rate(rate_or_dict, sf) + for hh_id in np.unique(ent_hh_ids[bm]): + hh_mask = bm & (ent_hh_ids == hh_id) + rng = seeded_rng( + takeup_var, + salt=f"{blk}:{int(hh_id)}", + ) + draws = rng.random(int(hh_mask.sum())) + ent_takeup[hh_mask] = draws < rate + + ent_values = (ent_eligible * ent_takeup).astype(np.float32) + + hh_result = np.zeros(n_records, dtype=np.float32) + np.add.at(hh_result, ent_hh, ent_values) + hh_vars[tvar] = hh_result + + if tvar in person_vars: + pidx = entity_to_person_idx[entity_level] + person_vars[tvar] = ent_values[pidx] + + # Build COO entries for every target row + mask_cache: dict = {} + count_cache: dict = {} + rows_list: list = [] + cols_list: list = [] + vals_list: list = [] + + for row_idx in range(n_targets): + variable = target_variables[row_idx] + geo_level, geo_id = target_geo_info[row_idx] + non_geo = non_geo_constraints_list[row_idx] + + if geo_level == "district": + all_geo_cols = cd_to_cols.get( + str(geo_id), + np.array([], dtype=np.int64), + ) + elif geo_level == "state": + all_geo_cols = state_to_cols.get( + int(geo_id), + np.array([], dtype=np.int64), + ) + else: + all_geo_cols = np.arange(n_total) + + clone_cols = all_geo_cols[ + (all_geo_cols >= col_start) & (all_geo_cols < col_end) + ] + if len(clone_cols) == 0: + continue + + rec_indices = clone_cols - col_start + + constraint_key = tuple( + sorted( + ( + c["variable"], + c["operation"], + c["value"], + ) + for c in non_geo + ) + ) + + if variable.endswith("_count"): + vkey = (variable, constraint_key) + if vkey not in count_cache: + count_cache[vkey] = _calculate_target_values_standalone( + variable, + non_geo, + n_records, + hh_vars, + person_vars, + entity_rel, + household_ids, + variable_entity_map, + ) + values = count_cache[vkey] + else: + if variable not in hh_vars: + continue + if constraint_key not in mask_cache: + mask_cache[constraint_key] = _evaluate_constraints_standalone( + non_geo, + person_vars, + entity_rel, + household_ids, + n_records, + ) + mask = mask_cache[constraint_key] + values = hh_vars[variable] * mask + + vals = values[rec_indices] + nonzero = vals != 0 + if nonzero.any(): + rows_list.append( + np.full( + nonzero.sum(), + row_idx, + dtype=np.int32, + ) + ) + cols_list.append(clone_cols[nonzero].astype(np.int32)) + vals_list.append(vals[nonzero]) + + # Write COO + if rows_list: + cr = np.concatenate(rows_list) + cc = np.concatenate(cols_list) + cv = np.concatenate(vals_list) + else: + cr = np.array([], dtype=np.int32) + cc = np.array([], dtype=np.int32) + cv = np.array([], dtype=np.float32) + + np.savez_compressed(cache_path, rows=cr, cols=cc, vals=cv) + return clone_idx, len(cv) + class UnifiedMatrixBuilder: """Build sparse calibration matrix for cloned CPS records. @@ -87,6 +757,571 @@ def _build_entity_relationship(self, sim) -> pd.DataFrame: ) return self._entity_rel_cache + # --------------------------------------------------------------- + # Per-state precomputation + # --------------------------------------------------------------- + + def _build_state_values( + self, + sim, + target_vars: set, + constraint_vars: set, + geography, + rerandomize_takeup: bool = True, + workers: int = 1, + ) -> dict: + """Precompute household/person/entity values per state. + + Creates a fresh Microsimulation per state to prevent + cross-state cache pollution (stale intermediate values + from one state leaking into another's calculations). + + County-dependent variables (e.g. aca_ptc) are computed + here as a state-level fallback; county-level overrides + are applied later via ``_build_county_values``. + + Args: + sim: Microsimulation instance (unused; kept for API + compatibility). + target_vars: Set of target variable names. + constraint_vars: Set of constraint variable names. + geography: GeographyAssignment with state_fips. + rerandomize_takeup: If True, force takeup=True and + also store entity-level eligible amounts for + takeup-affected targets. + workers: Number of parallel worker processes. + When >1, uses ProcessPoolExecutor. + + Returns: + {state_fips: { + 'hh': {var: array}, + 'person': {var: array}, + 'entity': {var: array} # only if rerandomize + }} + """ + from policyengine_us_data.utils.takeup import ( + TAKEUP_AFFECTED_TARGETS, + ) + + unique_states = sorted(set(int(s) for s in geography.state_fips)) + n_hh = geography.n_records + + logger.info( + "Per-state precomputation: %d states, " + "%d hh vars, %d constraint vars " + "(fresh sim per state, workers=%d)", + len(unique_states), + len([v for v in target_vars if not v.endswith("_count")]), + len(constraint_vars), + workers, + ) + + # Identify takeup-affected targets before the state loop + affected_targets = {} + if rerandomize_takeup: + for tvar in target_vars: + for key, info in TAKEUP_AFFECTED_TARGETS.items(): + if tvar == key or tvar.startswith(key): + affected_targets[tvar] = info + break + + # Convert sets to sorted lists for deterministic iteration + target_vars_list = sorted(target_vars) + constraint_vars_list = sorted(constraint_vars) + + state_values = {} + + if workers > 1: + from concurrent.futures import ( + ProcessPoolExecutor, + as_completed, + ) + + logger.info( + "Parallel state precomputation with %d workers", + workers, + ) + with ProcessPoolExecutor(max_workers=workers) as pool: + futures = { + pool.submit( + _compute_single_state, + self.dataset_path, + self.time_period, + st, + n_hh, + target_vars_list, + constraint_vars_list, + rerandomize_takeup, + affected_targets, + ): st + for st in unique_states + } + completed = 0 + for future in as_completed(futures): + st = futures[future] + try: + sf, vals = future.result() + state_values[sf] = vals + completed += 1 + if completed % 10 == 0 or completed == 1: + logger.info( + "State %d/%d complete", + completed, + len(unique_states), + ) + except Exception as exc: + for f in futures: + f.cancel() + raise RuntimeError( + f"State {st} failed: {exc}" + ) from exc + else: + from policyengine_us import Microsimulation + from policyengine_us_data.utils.takeup import ( + SIMPLE_TAKEUP_VARS, + ) + + for i, state in enumerate(unique_states): + state_sim = Microsimulation(dataset=self.dataset_path) + + if rerandomize_takeup: + for spec in SIMPLE_TAKEUP_VARS: + entity = spec["entity"] + n_ent = len( + state_sim.calculate( + f"{entity}_id", map_to=entity + ).values + ) + state_sim.set_input( + spec["variable"], + self.time_period, + np.ones(n_ent, dtype=bool), + ) + + state_sim.set_input( + "state_fips", + self.time_period, + np.full(n_hh, state, dtype=np.int32), + ) + for var in get_calculated_variables(state_sim): + state_sim.delete_arrays(var) + + hh = {} + for var in target_vars: + if var.endswith("_count"): + continue + try: + hh[var] = state_sim.calculate( + var, + self.time_period, + map_to="household", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate '%s' " "for state %d: %s", + var, + state, + exc, + ) + + person = {} + for var in constraint_vars: + try: + person[var] = state_sim.calculate( + var, + self.time_period, + map_to="person", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate constraint " + "'%s' for state %d: %s", + var, + state, + exc, + ) + + entity_vals = {} + if rerandomize_takeup: + for tvar, info in affected_targets.items(): + entity_level = info["entity"] + try: + entity_vals[tvar] = state_sim.calculate( + tvar, + self.time_period, + map_to=entity_level, + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate entity-level " + "'%s' (map_to=%s) for " + "state %d: %s", + tvar, + entity_level, + state, + exc, + ) + + state_values[state] = { + "hh": hh, + "person": person, + "entity": entity_vals, + } + if (i + 1) % 10 == 0 or i == 0: + logger.info( + "State %d/%d complete", + i + 1, + len(unique_states), + ) + + logger.info( + "Per-state precomputation done: %d states", + len(state_values), + ) + return state_values + + def _build_county_values( + self, + sim, + county_dep_targets: set, + geography, + rerandomize_takeup: bool = True, + county_level: bool = True, + workers: int = 1, + ) -> dict: + """Precompute county-dependent variable values per county. + + Only iterates over COUNTY_DEPENDENT_VARS that actually + benefit from per-county computation. All other target + variables use state-level values from _build_state_values. + + Creates a fresh Microsimulation per state group to prevent + cross-state cache pollution. Counties within the same state + share a simulation since within-state recalculation is clean + (only cross-state switches cause pollution). + + When county_level=False, returns an empty dict immediately + (all values come from state-level precomputation). + + Args: + sim: Microsimulation instance (unused; kept for API + compatibility). + county_dep_targets: Subset of target vars that depend + on county (intersection of targets with + COUNTY_DEPENDENT_VARS). + geography: GeographyAssignment with county_fips. + rerandomize_takeup: If True, force takeup=True and + also store entity-level eligible amounts for + takeup-affected targets. + county_level: If True, iterate counties within each + state. If False, return empty dict (skip county + computation entirely). + workers: Number of parallel worker processes. + When >1, uses ProcessPoolExecutor. + + Returns: + {county_fips_str: { + 'hh': {var: array}, + 'entity': {var: array} + }} + """ + if not county_level or not county_dep_targets: + if not county_level: + logger.info( + "County-level computation disabled " "(skip-county mode)" + ) + else: + logger.info( + "No county-dependent target vars; " + "skipping county precomputation" + ) + return {} + + from policyengine_us_data.utils.takeup import ( + TAKEUP_AFFECTED_TARGETS, + ) + + unique_counties = sorted(set(geography.county_fips)) + n_hh = geography.n_records + + state_to_counties = defaultdict(list) + for county in unique_counties: + state_to_counties[int(county[:2])].append(county) + + logger.info( + "Per-county precomputation: %d counties in %d " + "states, %d county-dependent vars " + "(fresh sim per state, workers=%d)", + len(unique_counties), + len(state_to_counties), + len(county_dep_targets), + workers, + ) + + affected_targets = {} + if rerandomize_takeup: + for tvar in county_dep_targets: + for key, info in TAKEUP_AFFECTED_TARGETS.items(): + if tvar == key or tvar.startswith(key): + affected_targets[tvar] = info + break + + # Convert to sorted list for deterministic iteration + county_dep_targets_list = sorted(county_dep_targets) + + county_values = {} + + if workers > 1: + from concurrent.futures import ( + ProcessPoolExecutor, + as_completed, + ) + + logger.info( + "Parallel county precomputation with " + "%d workers (%d state groups)", + workers, + len(state_to_counties), + ) + with ProcessPoolExecutor(max_workers=workers) as pool: + futures = { + pool.submit( + _compute_single_state_group_counties, + self.dataset_path, + self.time_period, + sf, + counties, + n_hh, + county_dep_targets_list, + rerandomize_takeup, + affected_targets, + ): sf + for sf, counties in sorted(state_to_counties.items()) + } + completed = 0 + county_count = 0 + for future in as_completed(futures): + sf = futures[future] + try: + results = future.result() + for cfips, vals in results: + county_values[cfips] = vals + county_count += 1 + completed += 1 + if county_count % 500 == 0 or completed == 1: + logger.info( + "County %d/%d complete " + "(%d/%d state groups)", + county_count, + len(unique_counties), + completed, + len(state_to_counties), + ) + except Exception as exc: + for f in futures: + f.cancel() + raise RuntimeError( + f"State group {sf} failed: " f"{exc}" + ) from exc + else: + from policyengine_us import Microsimulation + from policyengine_us_data.utils.takeup import ( + SIMPLE_TAKEUP_VARS, + ) + + county_count = 0 + for state_fips, counties in sorted(state_to_counties.items()): + state_sim = Microsimulation(dataset=self.dataset_path) + + if rerandomize_takeup: + for spec in SIMPLE_TAKEUP_VARS: + entity = spec["entity"] + n_ent = len( + state_sim.calculate( + f"{entity}_id", + map_to=entity, + ).values + ) + state_sim.set_input( + spec["variable"], + self.time_period, + np.ones(n_ent, dtype=bool), + ) + + state_sim.set_input( + "state_fips", + self.time_period, + np.full(n_hh, state_fips, dtype=np.int32), + ) + + for county_fips in counties: + county_idx = get_county_enum_index_from_fips(county_fips) + state_sim.set_input( + "county", + self.time_period, + np.full( + n_hh, + county_idx, + dtype=np.int32, + ), + ) + for var in get_calculated_variables(state_sim): + if var != "county": + state_sim.delete_arrays(var) + + hh = {} + for var in county_dep_targets: + if var.endswith("_count"): + continue + try: + hh[var] = state_sim.calculate( + var, + self.time_period, + map_to="household", + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate '%s' " "for county %s: %s", + var, + county_fips, + exc, + ) + + entity_vals = {} + if rerandomize_takeup: + for ( + tvar, + info, + ) in affected_targets.items(): + entity_level = info["entity"] + try: + entity_vals[tvar] = state_sim.calculate( + tvar, + self.time_period, + map_to=entity_level, + ).values.astype(np.float32) + except Exception as exc: + logger.warning( + "Cannot calculate " + "entity-level '%s' " + "for county %s: %s", + tvar, + county_fips, + exc, + ) + + county_values[county_fips] = { + "hh": hh, + "entity": entity_vals, + } + county_count += 1 + if county_count % 500 == 0 or county_count == 1: + logger.info( + "County %d/%d complete", + county_count, + len(unique_counties), + ) + + logger.info( + "Per-county precomputation done: %d counties", + len(county_values), + ) + return county_values + + def _assemble_clone_values( + self, + state_values: dict, + clone_states: np.ndarray, + person_hh_indices: np.ndarray, + target_vars: set, + constraint_vars: set, + county_values: dict = None, + clone_counties: np.ndarray = None, + county_dependent_vars: set = None, + ) -> tuple: + """Assemble per-clone values from state/county precomputation. + + For each target variable, selects values from either + county_values (if the var is county-dependent) or + state_values (otherwise) using numpy fancy indexing. + + Args: + state_values: Output of _build_state_values. + clone_states: State FIPS per record for this clone. + person_hh_indices: Maps person index to household + index (0..n_records-1). + target_vars: Set of target variable names. + constraint_vars: Set of constraint variable names. + county_values: Output of _build_county_values. + clone_counties: County FIPS per record for this + clone (str array). + county_dependent_vars: Set of var names that should + be looked up by county instead of state. + + Returns: + (hh_vars, person_vars) where hh_vars maps variable + name to household-level float32 array and person_vars + maps constraint variable name to person-level array. + """ + n_records = len(clone_states) + n_persons = len(person_hh_indices) + person_states = clone_states[person_hh_indices] + unique_clone_states = np.unique(clone_states) + cdv = county_dependent_vars or set() + + # Pre-compute masks to avoid recomputing per variable + state_masks = {int(s): clone_states == s for s in unique_clone_states} + unique_person_states = np.unique(person_states) + person_state_masks = { + int(s): person_states == s for s in unique_person_states + } + county_masks = {} + unique_counties = None + if clone_counties is not None and county_values: + unique_counties = np.unique(clone_counties) + county_masks = {c: clone_counties == c for c in unique_counties} + + hh_vars = {} + for var in target_vars: + if var.endswith("_count"): + continue + if var in cdv and county_values and clone_counties is not None: + first_county = unique_counties[0] + if var not in county_values.get(first_county, {}).get( + "hh", {} + ): + continue + arr = np.empty(n_records, dtype=np.float32) + for county in unique_counties: + mask = county_masks[county] + county_hh = county_values.get(county, {}).get("hh", {}) + if var in county_hh: + arr[mask] = county_hh[var][mask] + else: + st = int(county[:2]) + arr[mask] = state_values[st]["hh"][var][mask] + hh_vars[var] = arr + else: + if var not in state_values[unique_clone_states[0]]["hh"]: + continue + arr = np.empty(n_records, dtype=np.float32) + for state in unique_clone_states: + mask = state_masks[int(state)] + arr[mask] = state_values[int(state)]["hh"][var][mask] + hh_vars[var] = arr + + person_vars = {} + for var in constraint_vars: + if var not in state_values[unique_clone_states[0]]["person"]: + continue + arr = np.empty(n_persons, dtype=np.float32) + for state in unique_person_states: + mask = person_state_masks[int(state)] + arr[mask] = state_values[int(state)]["person"][var][mask] + person_vars[var] = arr + + return hh_vars, person_vars + # --------------------------------------------------------------- # Constraint evaluation # --------------------------------------------------------------- @@ -131,6 +1366,38 @@ def _evaluate_constraints_entity_aware( ).values return np.array([hh_mask.get(hid, False) for hid in household_ids]) + def _evaluate_constraints_from_values( + self, + constraints: List[dict], + person_vars: Dict[str, np.ndarray], + entity_rel: pd.DataFrame, + household_ids: np.ndarray, + n_households: int, + ) -> np.ndarray: + """Evaluate constraints from precomputed person-level + values, aggregate to household level via .any().""" + if not constraints: + return np.ones(n_households, dtype=bool) + + n_persons = len(entity_rel) + person_mask = np.ones(n_persons, dtype=bool) + + for c in constraints: + var = c["variable"] + if var not in person_vars: + logger.warning( + "Constraint var '%s' not in precomputed " "person_vars", + var, + ) + return np.zeros(n_households, dtype=bool) + vals = person_vars[var] + person_mask &= apply_op(vals, c["operation"], c["value"]) + + df = entity_rel.copy() + df["satisfies"] = person_mask + hh_mask = df.groupby("household_id")["satisfies"].any() + return np.array([hh_mask.get(hid, False) for hid in household_ids]) + # --------------------------------------------------------------- # Database queries # --------------------------------------------------------------- @@ -545,6 +1812,85 @@ def _calculate_target_values( dtype=np.float32, ) + def _calculate_target_values_from_values( + self, + target_variable: str, + non_geo_constraints: List[dict], + n_households: int, + hh_vars: Dict[str, np.ndarray], + person_vars: Dict[str, np.ndarray], + entity_rel: pd.DataFrame, + household_ids: np.ndarray, + tax_benefit_system, + ) -> np.ndarray: + """Calculate per-household target values from precomputed + arrays. + + Same logic as _calculate_target_values but reads from + hh_vars/person_vars instead of calling sim.calculate(). + """ + is_count = target_variable.endswith("_count") + + if not is_count: + mask = self._evaluate_constraints_from_values( + non_geo_constraints, + person_vars, + entity_rel, + household_ids, + n_households, + ) + vals = hh_vars.get(target_variable) + if vals is None: + return np.zeros(n_households, dtype=np.float32) + return (vals * mask).astype(np.float32) + + # Count target: entity-aware counting + n_persons = len(entity_rel) + person_mask = np.ones(n_persons, dtype=bool) + + for c in non_geo_constraints: + var = c["variable"] + if var not in person_vars: + return np.zeros(n_households, dtype=np.float32) + cv = person_vars[var] + person_mask &= apply_op(cv, c["operation"], c["value"]) + + target_entity = tax_benefit_system.variables[ + target_variable + ].entity.key + + if target_entity == "household": + if non_geo_constraints: + mask = self._evaluate_constraints_from_values( + non_geo_constraints, + person_vars, + entity_rel, + household_ids, + n_households, + ) + return mask.astype(np.float32) + return np.ones(n_households, dtype=np.float32) + + if target_entity == "person": + er = entity_rel.copy() + er["satisfies"] = person_mask + filtered = er[er["satisfies"]] + counts = filtered.groupby("household_id")["person_id"].nunique() + else: + eid_col = f"{target_entity}_id" + er = entity_rel.copy() + er["satisfies"] = person_mask + entity_ok = er.groupby(eid_col)["satisfies"].any() + unique = er[["household_id", eid_col]].drop_duplicates() + unique["entity_ok"] = unique[eid_col].map(entity_ok) + filtered = unique[unique["entity_ok"]] + counts = filtered.groupby("household_id")[eid_col].nunique() + + return np.array( + [counts.get(hid, 0) for hid in household_ids], + dtype=np.float32, + ) + # --------------------------------------------------------------- # Clone simulation # --------------------------------------------------------------- @@ -614,6 +1960,9 @@ def build_matrix( hierarchical_domains: Optional[List[str]] = None, cache_dir: Optional[str] = None, sim_modifier=None, + rerandomize_takeup: bool = True, + county_level: bool = True, + workers: int = 1, ) -> Tuple[pd.DataFrame, sparse.csr_matrix, List[str]]: """Build sparse calibration matrix. @@ -635,6 +1984,13 @@ def build_matrix( called per clone after state_fips is set but before cache clearing. Use for takeup re-randomization. + rerandomize_takeup: If True, use geo-salted + entity-level takeup draws instead of base h5 + takeup values for takeup-affected targets. + county_level: If True (default), iterate counties + within each state during precomputation. If + False, compute once per state and alias to all + counties (faster for county-invariant vars). Returns: (targets_df, X_sparse, target_names) @@ -720,156 +2076,463 @@ def build_matrix( unique_variables = set(targets_df["variable"].values) - # 5. Clone loop + # 5a. Collect unique constraint variables + unique_constraint_vars = set() + for constraints in non_geo_constraints_list: + for c in constraints: + unique_constraint_vars.add(c["variable"]) + + # 5b. Per-state precomputation (51 sims on one object) + self._entity_rel_cache = None + state_values = self._build_state_values( + sim, + unique_variables, + unique_constraint_vars, + geography, + rerandomize_takeup=rerandomize_takeup, + workers=workers, + ) + + # 5b-county. Per-county precomputation for county-dependent vars + county_dep_targets = unique_variables & COUNTY_DEPENDENT_VARS + county_values = self._build_county_values( + sim, + county_dep_targets, + geography, + rerandomize_takeup=rerandomize_takeup, + county_level=county_level, + workers=workers, + ) + + # 5c. State-independent structures (computed once) + entity_rel = self._build_entity_relationship(sim) + household_ids = sim.calculate( + "household_id", map_to="household" + ).values + person_hh_ids = sim.calculate("household_id", map_to="person").values + hh_id_to_idx = {int(hid): idx for idx, hid in enumerate(household_ids)} + person_hh_indices = np.array( + [hh_id_to_idx[int(hid)] for hid in person_hh_ids] + ) + tax_benefit_system = sim.tax_benefit_system + + # Pre-extract entity keys so workers don't need + # the unpicklable TaxBenefitSystem object. + variable_entity_map: Dict[str, str] = {} + for var in unique_variables: + if var.endswith("_count") and var in tax_benefit_system.variables: + variable_entity_map[var] = tax_benefit_system.variables[ + var + ].entity.key + + # 5c-extra: Entity-to-household index maps for takeup + affected_target_info = {} + if rerandomize_takeup: + from policyengine_us_data.utils.takeup import ( + TAKEUP_AFFECTED_TARGETS, + _resolve_rate, + ) + from policyengine_us_data.parameters import ( + load_take_up_rate, + ) + from policyengine_us_data.utils.randomness import ( + seeded_rng, + ) + + # Build entity-to-household index arrays + spm_to_hh_id = ( + entity_rel.groupby("spm_unit_id")["household_id"] + .first() + .to_dict() + ) + spm_ids = sim.calculate("spm_unit_id", map_to="spm_unit").values + spm_hh_idx = np.array( + [hh_id_to_idx[int(spm_to_hh_id[int(sid)])] for sid in spm_ids] + ) + + tu_to_hh_id = ( + entity_rel.groupby("tax_unit_id")["household_id"] + .first() + .to_dict() + ) + tu_ids = sim.calculate("tax_unit_id", map_to="tax_unit").values + tu_hh_idx = np.array( + [hh_id_to_idx[int(tu_to_hh_id[int(tid)])] for tid in tu_ids] + ) + + entity_hh_idx_map = { + "spm_unit": spm_hh_idx, + "tax_unit": tu_hh_idx, + "person": person_hh_indices, + } + + entity_to_person_idx = {} + for entity_level in ("spm_unit", "tax_unit"): + ent_ids = sim.calculate( + f"{entity_level}_id", + map_to=entity_level, + ).values + ent_id_to_idx = { + int(eid): idx for idx, eid in enumerate(ent_ids) + } + person_ent_ids = entity_rel[f"{entity_level}_id"].values + entity_to_person_idx[entity_level] = np.array( + [ent_id_to_idx[int(eid)] for eid in person_ent_ids] + ) + entity_to_person_idx["person"] = np.arange(len(entity_rel)) + + for tvar in unique_variables: + for key, info in TAKEUP_AFFECTED_TARGETS.items(): + if tvar == key: + affected_target_info[tvar] = info + break + + logger.info( + "Block-level takeup enabled, " "%d affected target vars", + len(affected_target_info), + ) + + # Pre-compute takeup rates (constant across clones) + precomputed_rates = {} + for tvar, info in affected_target_info.items(): + rk = info["rate_key"] + if rk not in precomputed_rates: + precomputed_rates[rk] = load_take_up_rate( + rk, self.time_period + ) + + # 5d. Clone loop from pathlib import Path - clone_dir = Path(cache_dir) if cache_dir else None - if clone_dir: + if workers > 1: + # ---- Parallel clone processing ---- + import concurrent.futures + import tempfile + + if cache_dir: + clone_dir = Path(cache_dir) + else: + clone_dir = Path(tempfile.mkdtemp(prefix="clone_coo_")) clone_dir.mkdir(parents=True, exist_ok=True) - self._entity_rel_cache = None + target_variables = [ + str(targets_df.iloc[i]["variable"]) for i in range(n_targets) + ] + + shared_data = { + "geography_state_fips": geography.state_fips, + "geography_county_fips": geography.county_fips, + "geography_block_geoid": geography.block_geoid, + "state_values": state_values, + "county_values": county_values, + "person_hh_indices": person_hh_indices, + "unique_variables": unique_variables, + "unique_constraint_vars": unique_constraint_vars, + "county_dep_targets": county_dep_targets, + "target_variables": target_variables, + "target_geo_info": target_geo_info, + "non_geo_constraints_list": (non_geo_constraints_list), + "n_records": n_records, + "n_total": n_total, + "n_targets": n_targets, + "state_to_cols": state_to_cols, + "cd_to_cols": cd_to_cols, + "entity_rel": entity_rel, + "household_ids": household_ids, + "variable_entity_map": variable_entity_map, + "rerandomize_takeup": rerandomize_takeup, + "affected_target_info": affected_target_info, + } + if rerandomize_takeup and affected_target_info: + shared_data["entity_hh_idx_map"] = entity_hh_idx_map + shared_data["entity_to_person_idx"] = entity_to_person_idx + shared_data["precomputed_rates"] = precomputed_rates + + logger.info( + "Starting parallel clone processing: " "%d clones, %d workers", + n_clones, + workers, + ) - for clone_idx in range(n_clones): + futures: dict = {} + with concurrent.futures.ProcessPoolExecutor( + max_workers=workers, + initializer=_init_clone_worker, + initargs=(shared_data,), + ) as pool: + for ci in range(n_clones): + coo_path = str(clone_dir / f"clone_{ci:04d}.npz") + if Path(coo_path).exists(): + logger.info( + "Clone %d/%d cached.", + ci + 1, + n_clones, + ) + continue + cs = ci * n_records + ce = cs + n_records + fut = pool.submit( + _process_single_clone, + ci, + cs, + ce, + coo_path, + ) + futures[fut] = ci + + for fut in concurrent.futures.as_completed(futures): + ci = futures[fut] + try: + _, nnz = fut.result() + if (ci + 1) % 50 == 0: + logger.info( + "Clone %d/%d done " "(%d nnz).", + ci + 1, + n_clones, + nnz, + ) + except Exception as exc: + for f in futures: + f.cancel() + raise RuntimeError( + f"Clone {ci} failed: {exc}" + ) from exc + + else: + # ---- Sequential clone processing (unchanged) ---- + clone_dir = Path(cache_dir) if cache_dir else None if clone_dir: - coo_path = clone_dir / f"clone_{clone_idx:04d}.npz" - if coo_path.exists(): + clone_dir.mkdir(parents=True, exist_ok=True) + + for clone_idx in range(n_clones): + if clone_dir: + coo_path = clone_dir / f"clone_{clone_idx:04d}.npz" + if coo_path.exists(): + logger.info( + "Clone %d/%d cached, " "skipping.", + clone_idx + 1, + n_clones, + ) + continue + + col_start = clone_idx * n_records + col_end = col_start + n_records + clone_states = geography.state_fips[col_start:col_end] + clone_counties = geography.county_fips[col_start:col_end] + + if (clone_idx + 1) % 50 == 0 or clone_idx == 0: logger.info( - "Clone %d/%d cached, skipping.", + "Assembling clone %d/%d " + "(cols %d-%d, " + "%d unique states)...", clone_idx + 1, n_clones, + col_start, + col_end - 1, + len(np.unique(clone_states)), ) - continue - col_start = clone_idx * n_records - col_end = col_start + n_records - clone_states = geography.state_fips[col_start:col_end] + hh_vars, person_vars = self._assemble_clone_values( + state_values, + clone_states, + person_hh_indices, + unique_variables, + unique_constraint_vars, + county_values=county_values, + clone_counties=clone_counties, + county_dependent_vars=(county_dep_targets), + ) - logger.info( - "Processing clone %d/%d " "(cols %d-%d, %d unique states)...", - clone_idx + 1, - n_clones, - col_start, - col_end - 1, - len(np.unique(clone_states)), - ) + # Apply geo-specific entity-level takeup + # for affected target variables + if rerandomize_takeup and affected_target_info: + clone_blocks = geography.block_geoid[col_start:col_end] + for ( + tvar, + info, + ) in affected_target_info.items(): + if tvar.endswith("_count"): + continue + entity_level = info["entity"] + takeup_var = info["takeup_var"] + ent_hh = entity_hh_idx_map[entity_level] + n_ent = len(ent_hh) + + ent_states = clone_states[ent_hh] + + ent_eligible = np.zeros(n_ent, dtype=np.float32) + if tvar in county_dep_targets and county_values: + ent_counties = clone_counties[ent_hh] + for cfips in np.unique(ent_counties): + m = ent_counties == cfips + cv = county_values.get(cfips, {}).get( + "entity", {} + ) + if tvar in cv: + ent_eligible[m] = cv[tvar][m] + else: + st = int(cfips[:2]) + sv = state_values[st]["entity"] + if tvar in sv: + ent_eligible[m] = sv[tvar][m] + else: + for st in np.unique(ent_states): + m = ent_states == st + sv = state_values[int(st)]["entity"] + if tvar in sv: + ent_eligible[m] = sv[tvar][m] + + ent_blocks = clone_blocks[ent_hh] + ent_hh_ids = household_ids[ent_hh] + + ent_takeup = np.zeros(n_ent, dtype=bool) + rate_key = info["rate_key"] + rate_or_dict = precomputed_rates[rate_key] + for blk in np.unique(ent_blocks): + bm = ent_blocks == blk + sf = int(blk[:2]) + rate = _resolve_rate(rate_or_dict, sf) + for hh_id in np.unique(ent_hh_ids[bm]): + hh_mask = bm & (ent_hh_ids == hh_id) + rng = seeded_rng( + takeup_var, + salt=(f"{blk}:" f"{int(hh_id)}"), + ) + draws = rng.random(int(hh_mask.sum())) + ent_takeup[hh_mask] = draws < rate + + ent_values = (ent_eligible * ent_takeup).astype( + np.float32 + ) - var_values, clone_sim = self._simulate_clone( - clone_states, - n_records, - unique_variables, - sim_modifier=sim_modifier, - clone_idx=clone_idx, - ) + hh_result = np.zeros(n_records, dtype=np.float32) + np.add.at(hh_result, ent_hh, ent_values) + hh_vars[tvar] = hh_result - mask_cache: Dict[tuple, np.ndarray] = {} - count_cache: Dict[tuple, np.ndarray] = {} + if tvar in person_vars: + pidx = entity_to_person_idx[entity_level] + person_vars[tvar] = ent_values[pidx] - rows_list: list = [] - cols_list: list = [] - vals_list: list = [] + mask_cache: Dict[tuple, np.ndarray] = {} + count_cache: Dict[tuple, np.ndarray] = {} - for row_idx in range(n_targets): - variable = str(targets_df.iloc[row_idx]["variable"]) - geo_level, geo_id = target_geo_info[row_idx] - non_geo = non_geo_constraints_list[row_idx] + rows_list: list = [] + cols_list: list = [] + vals_list: list = [] - # Geographic column selection - if geo_level == "district": - all_geo_cols = cd_to_cols.get( - str(geo_id), - np.array([], dtype=np.int64), - ) - elif geo_level == "state": - all_geo_cols = state_to_cols.get( - int(geo_id), - np.array([], dtype=np.int64), - ) - else: - all_geo_cols = np.arange(n_total) + for row_idx in range(n_targets): + variable = str(targets_df.iloc[row_idx]["variable"]) + geo_level, geo_id = target_geo_info[row_idx] + non_geo = non_geo_constraints_list[row_idx] - clone_cols = all_geo_cols[ - (all_geo_cols >= col_start) & (all_geo_cols < col_end) - ] - if len(clone_cols) == 0: - continue + if geo_level == "district": + all_geo_cols = cd_to_cols.get( + str(geo_id), + np.array([], dtype=np.int64), + ) + elif geo_level == "state": + all_geo_cols = state_to_cols.get( + int(geo_id), + np.array([], dtype=np.int64), + ) + else: + all_geo_cols = np.arange(n_total) - rec_indices = clone_cols - col_start + clone_cols = all_geo_cols[ + (all_geo_cols >= col_start) & (all_geo_cols < col_end) + ] + if len(clone_cols) == 0: + continue + + rec_indices = clone_cols - col_start - constraint_key = tuple( - sorted( - ( - c["variable"], - c["operation"], - c["value"], + constraint_key = tuple( + sorted( + ( + c["variable"], + c["operation"], + c["value"], + ) + for c in non_geo ) - for c in non_geo ) - ) - if variable.endswith("_count"): - vkey = (variable, constraint_key) - if vkey not in count_cache: - count_cache[vkey] = self._calculate_target_values( - clone_sim, + if variable.endswith("_count"): + vkey = ( variable, - non_geo, - n_records, + constraint_key, ) - values = count_cache[vkey] - else: - if variable not in var_values: - continue - if constraint_key not in mask_cache: - mask_cache[constraint_key] = ( - self._evaluate_constraints_entity_aware( - clone_sim, - non_geo, - n_records, + if vkey not in count_cache: + count_cache[vkey] = ( + self._calculate_target_values_from_values( + variable, + non_geo, + n_records, + hh_vars, + person_vars, + entity_rel, + household_ids, + tax_benefit_system, + ) + ) + values = count_cache[vkey] + else: + if variable not in hh_vars: + continue + if constraint_key not in mask_cache: + mask_cache[constraint_key] = ( + self._evaluate_constraints_from_values( + non_geo, + person_vars, + entity_rel, + household_ids, + n_records, + ) + ) + mask = mask_cache[constraint_key] + values = hh_vars[variable] * mask + + vals = values[rec_indices] + nonzero = vals != 0 + if nonzero.any(): + rows_list.append( + np.full( + nonzero.sum(), + row_idx, + dtype=np.int32, ) ) - mask = mask_cache[constraint_key] - values = var_values[variable] * mask - - vals = values[rec_indices] - nonzero = vals != 0 - if nonzero.any(): - rows_list.append( - np.full( - nonzero.sum(), - row_idx, - dtype=np.int32, - ) + cols_list.append(clone_cols[nonzero].astype(np.int32)) + vals_list.append(vals[nonzero]) + + # Save COO entries + if rows_list: + cr = np.concatenate(rows_list) + cc = np.concatenate(cols_list) + cv = np.concatenate(vals_list) + else: + cr = np.array([], dtype=np.int32) + cc = np.array([], dtype=np.int32) + cv = np.array([], dtype=np.float32) + + if clone_dir: + np.savez_compressed( + str(coo_path), + rows=cr, + cols=cc, + vals=cv, ) - cols_list.append(clone_cols[nonzero].astype(np.int32)) - vals_list.append(vals[nonzero]) - - # Save COO entries - if rows_list: - cr = np.concatenate(rows_list) - cc = np.concatenate(cols_list) - cv = np.concatenate(vals_list) - else: - cr = np.array([], dtype=np.int32) - cc = np.array([], dtype=np.int32) - cv = np.array([], dtype=np.float32) - - if clone_dir: - np.savez_compressed( - str(coo_path), - rows=cr, - cols=cc, - vals=cv, - ) - logger.info( - "Clone %d: %d nonzero entries saved.", - clone_idx + 1, - len(cv), - ) - del var_values, clone_sim - else: - self._coo_parts[0].append(cr) - self._coo_parts[1].append(cc) - self._coo_parts[2].append(cv) + if (clone_idx + 1) % 50 == 0: + logger.info( + "Clone %d: %d nonzero " "entries saved.", + clone_idx + 1, + len(cv), + ) + del hh_vars, person_vars + else: + self._coo_parts[0].append(cr) + self._coo_parts[1].append(cc) + self._coo_parts[2].append(cv) # 6. Assemble sparse matrix from COO data logger.info("Assembling matrix from %d clones...", n_clones) diff --git a/policyengine_us_data/calibration/validate_package.py b/policyengine_us_data/calibration/validate_package.py new file mode 100644 index 00000000..523b0eca --- /dev/null +++ b/policyengine_us_data/calibration/validate_package.py @@ -0,0 +1,330 @@ +""" +Validate a calibration package before uploading to Modal. + +Usage: + python -m policyengine_us_data.calibration.validate_package [path] + [--n-hardest N] [--strict [RATIO]] +""" + +import argparse +import sys +from dataclasses import dataclass, field +from pathlib import Path +from typing import Optional + +import numpy as np +import pandas as pd + + +@dataclass +class ValidationResult: + n_targets: int + n_columns: int + nnz: int + density: float + metadata: dict + n_achievable: int + n_impossible: int + impossible_targets: pd.DataFrame + impossible_by_group: pd.DataFrame + hardest_targets: pd.DataFrame + group_summary: pd.DataFrame + strict_ratio: Optional[float] = None + strict_failures: int = 0 + + +def validate_package( + package: dict, + n_hardest: int = 10, + strict_ratio: float = None, +) -> ValidationResult: + X_sparse = package["X_sparse"] + targets_df = package["targets_df"] + target_names = package["target_names"] + metadata = package.get("metadata", {}) + + n_targets, n_columns = X_sparse.shape + nnz = X_sparse.nnz + density = nnz / (n_targets * n_columns) if n_targets * n_columns else 0 + + row_sums = np.array(X_sparse.sum(axis=1)).flatten() + achievable_mask = row_sums > 0 + n_achievable = int(achievable_mask.sum()) + n_impossible = n_targets - n_achievable + + impossible_idx = np.where(~achievable_mask)[0] + impossible_rows = targets_df.iloc[impossible_idx] + impossible_targets = pd.DataFrame( + { + "target_name": [target_names[i] for i in impossible_idx], + "domain_variable": impossible_rows["domain_variable"].values, + "variable": impossible_rows["variable"].values, + "geo_level": impossible_rows["geo_level"].values, + "geographic_id": impossible_rows["geographic_id"].values, + "target_value": impossible_rows["value"].values, + } + ) + impossible_by_group = ( + impossible_rows.groupby(["domain_variable", "variable", "geo_level"]) + .size() + .reset_index(name="count") + .sort_values("count", ascending=False) + .reset_index(drop=True) + ) + + target_values = targets_df["value"].values + achievable_idx = np.where(achievable_mask)[0] + if len(achievable_idx) > 0: + a_row_sums = row_sums[achievable_idx] + a_target_vals = target_values[achievable_idx] + with np.errstate(divide="ignore", invalid="ignore"): + ratios = np.where( + a_target_vals != 0, + a_row_sums / a_target_vals, + np.inf, + ) + k = min(n_hardest, len(ratios)) + hardest_local_idx = np.argpartition(ratios, k)[:k] + hardest_local_idx = hardest_local_idx[ + np.argsort(ratios[hardest_local_idx]) + ] + hardest_global_idx = achievable_idx[hardest_local_idx] + + hardest_targets = pd.DataFrame( + { + "target_name": [target_names[i] for i in hardest_global_idx], + "domain_variable": targets_df["domain_variable"] + .iloc[hardest_global_idx] + .values, + "variable": targets_df["variable"] + .iloc[hardest_global_idx] + .values, + "geographic_id": targets_df["geographic_id"] + .iloc[hardest_global_idx] + .values, + "ratio": ratios[hardest_local_idx], + "row_sum": a_row_sums[hardest_local_idx], + "target_value": a_target_vals[hardest_local_idx], + } + ) + else: + hardest_targets = pd.DataFrame( + columns=[ + "target_name", + "domain_variable", + "variable", + "geographic_id", + "ratio", + "row_sum", + "target_value", + ] + ) + + group_summary = ( + targets_df.assign(achievable=achievable_mask) + .groupby(["domain_variable", "variable", "geo_level"]) + .agg(total=("value", "size"), ok=("achievable", "sum")) + .reset_index() + ) + group_summary["impossible"] = group_summary["total"] - group_summary["ok"] + group_summary["ok"] = group_summary["ok"].astype(int) + group_summary = group_summary.sort_values( + ["domain_variable", "variable", "geo_level"] + ).reset_index(drop=True) + + strict_failures = 0 + if strict_ratio is not None and len(achievable_idx) > 0: + strict_failures = int((ratios < strict_ratio).sum()) + + return ValidationResult( + n_targets=n_targets, + n_columns=n_columns, + nnz=nnz, + density=density, + metadata=metadata, + n_achievable=n_achievable, + n_impossible=n_impossible, + impossible_targets=impossible_targets, + impossible_by_group=impossible_by_group, + hardest_targets=hardest_targets, + group_summary=group_summary, + strict_ratio=strict_ratio, + strict_failures=strict_failures, + ) + + +def format_report(result: ValidationResult, package_path: str = None) -> str: + lines = ["", "=== Calibration Package Validation ===", ""] + + if package_path: + lines.append(f"Package: {package_path}") + meta = result.metadata + if meta.get("created_at"): + lines.append(f"Created: {meta['created_at']}") + if meta.get("dataset_path"): + lines.append(f"Dataset: {meta['dataset_path']}") + lines.append("") + + lines.append( + f"Matrix: {result.n_targets:,} targets" + f" x {result.n_columns:,} columns" + ) + lines.append(f"Non-zero: {result.nnz:,} (density: {result.density:.6f})") + if meta.get("n_clones"): + parts = [f"Clones: {meta['n_clones']}"] + if meta.get("n_records"): + parts.append(f"Records: {meta['n_records']:,}") + if meta.get("seed") is not None: + parts.append(f"Seed: {meta['seed']}") + lines.append(", ".join(parts)) + lines.append("") + + pct = ( + 100 * result.n_achievable / result.n_targets if result.n_targets else 0 + ) + pct_imp = 100 - pct + lines.append("--- Achievability ---") + lines.append( + f"Achievable: {result.n_achievable:>6,}" + f" / {result.n_targets:,} ({pct:.1f}%)" + ) + lines.append( + f"Impossible: {result.n_impossible:>6,}" + f" / {result.n_targets:,} ({pct_imp:.1f}%)" + ) + lines.append("") + + if len(result.impossible_targets) > 0: + lines.append("--- Impossible Targets ---") + for _, row in result.impossible_targets.iterrows(): + lines.append( + f" {row['target_name']:<60s}" + f" {row['target_value']:>14,.0f}" + ) + lines.append("") + + if len(result.impossible_by_group) > 1: + lines.append("--- Impossible Targets by Group ---") + for _, row in result.impossible_by_group.iterrows(): + lines.append( + f" {row['domain_variable']:<20s}" + f" {row['variable']:<25s}" + f" {row['geo_level']:<12s}" + f" {row['count']:>5d}" + ) + lines.append("") + + if len(result.hardest_targets) > 0: + n = len(result.hardest_targets) + lines.append( + f"--- Hardest Achievable Targets" f" ({n} lowest ratio) ---" + ) + for _, row in result.hardest_targets.iterrows(): + lines.append( + f" {row['target_name']:<50s}" + f" {row['ratio']:>10.4f}" + f" {row['row_sum']:>14,.0f}" + f" {row['target_value']:>14,.0f}" + ) + lines.append("") + + if len(result.group_summary) > 0: + lines.append("--- Group Summary ---") + lines.append( + f" {'domain':<20s} {'variable':<25s}" + f" {'geo_level':<12s}" + f" {'total':>6s} {'ok':>6s} {'impossible':>10s}" + ) + for _, row in result.group_summary.iterrows(): + lines.append( + f" {row['domain_variable']:<20s}" + f" {row['variable']:<25s}" + f" {row['geo_level']:<12s}" + f" {row['total']:>6d}" + f" {row['ok']:>6d}" + f" {row['impossible']:>10d}" + ) + lines.append("") + + if result.strict_ratio is not None: + lines.append( + f"Strict check (ratio < {result.strict_ratio}):" + f" {result.strict_failures} failures" + ) + lines.append("") + + if result.strict_ratio is not None and result.strict_failures > 0: + lines.append( + f"RESULT: FAIL ({result.strict_failures}" + f" targets below ratio {result.strict_ratio})" + ) + elif result.n_impossible > 0: + lines.append( + f"RESULT: FAIL ({result.n_impossible} impossible targets)" + ) + else: + lines.append("RESULT: PASS") + + return "\n".join(lines) + + +def main(): + parser = argparse.ArgumentParser( + description="Validate a calibration package" + ) + parser.add_argument( + "path", + nargs="?", + default=None, + help="Path to calibration_package.pkl", + ) + parser.add_argument( + "--n-hardest", + type=int, + default=10, + help="Number of hardest achievable targets to show", + ) + parser.add_argument( + "--strict", + nargs="?", + const=0.01, + type=float, + default=None, + metavar="RATIO", + help="Fail if any achievable target has ratio below RATIO" + " (default: 0.01)", + ) + args = parser.parse_args() + + if args.path is None: + from policyengine_us_data.storage import STORAGE_FOLDER + + path = STORAGE_FOLDER / "calibration" / "calibration_package.pkl" + else: + path = Path(args.path) + + if not path.exists(): + print(f"Error: package not found at {path}", file=sys.stderr) + sys.exit(1) + + from policyengine_us_data.calibration.unified_calibration import ( + load_calibration_package, + ) + + package = load_calibration_package(str(path)) + result = validate_package( + package, + n_hardest=args.n_hardest, + strict_ratio=args.strict, + ) + print(format_report(result, package_path=str(path))) + + if args.strict is not None and result.strict_failures > 0: + sys.exit(2) + elif result.n_impossible > 0: + sys.exit(1) + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/policyengine_us_data/datasets/cps/local_area_calibration/block_assignment.py b/policyengine_us_data/datasets/cps/local_area_calibration/block_assignment.py index 73b435f6..f4f2cc13 100644 --- a/policyengine_us_data/datasets/cps/local_area_calibration/block_assignment.py +++ b/policyengine_us_data/datasets/cps/local_area_calibration/block_assignment.py @@ -100,22 +100,33 @@ def _build_county_fips_to_enum() -> Dict[str, str]: return fips_to_enum -def get_county_enum_index_from_block(block_geoid: str) -> int: - """ - Get County enum index from block GEOID. +def get_county_enum_index_from_fips(county_fips: str) -> int: + """Get County enum index from 5-digit county FIPS. Args: - block_geoid: 15-digit census block GEOID + county_fips: 5-digit county FIPS code (e.g. "37183") Returns: Integer index into County enum, or UNKNOWN index if not found """ - county_fips = get_county_fips_from_block(block_geoid) fips_to_enum = _build_county_fips_to_enum() enum_name = fips_to_enum.get(county_fips, "UNKNOWN") return County._member_names_.index(enum_name) +def get_county_enum_index_from_block(block_geoid: str) -> int: + """Get County enum index from block GEOID. + + Args: + block_geoid: 15-digit census block GEOID + + Returns: + Integer index into County enum, or UNKNOWN index if not found + """ + county_fips = get_county_fips_from_block(block_geoid) + return get_county_enum_index_from_fips(county_fips) + + # === CBSA Lookup === @@ -508,6 +519,82 @@ def assign_geography_for_cd( } +def derive_geography_from_blocks( + block_geoids: np.ndarray, +) -> Dict[str, np.ndarray]: + """Derive all geography from pre-assigned block GEOIDs. + + Given an array of block GEOIDs (already assigned by + calibration), derives county, tract, state, CBSA, SLDU, + SLDL, place, VTD, PUMA, ZCTA, and county enum index. + + Args: + block_geoids: Array of 15-char block GEOID strings. + + Returns: + Dict with same keys as assign_geography_for_cd. + """ + county_fips = np.array( + [get_county_fips_from_block(b) for b in block_geoids] + ) + tract_geoids = np.array( + [get_tract_geoid_from_block(b) for b in block_geoids] + ) + state_fips = np.array([get_state_fips_from_block(b) for b in block_geoids]) + cbsa_codes = np.array([get_cbsa_from_county(c) or "" for c in county_fips]) + county_indices = np.array( + [get_county_enum_index_from_block(b) for b in block_geoids], + dtype=np.int32, + ) + + crosswalk = _load_block_crosswalk() + has_zcta = "zcta" in crosswalk.columns + + sldu_list = [] + sldl_list = [] + place_fips_list = [] + vtd_list = [] + puma_list = [] + zcta_list = [] + + for b in block_geoids: + if not crosswalk.empty and b in crosswalk.index: + row = crosswalk.loc[b] + sldu_list.append(row["sldu"] if pd.notna(row["sldu"]) else "") + sldl_list.append(row["sldl"] if pd.notna(row["sldl"]) else "") + place_fips_list.append( + row["place_fips"] if pd.notna(row["place_fips"]) else "" + ) + vtd_list.append(row["vtd"] if pd.notna(row["vtd"]) else "") + puma_list.append(row["puma"] if pd.notna(row["puma"]) else "") + if has_zcta: + zcta_list.append(row["zcta"] if pd.notna(row["zcta"]) else "") + else: + zcta_list.append("") + else: + sldu_list.append("") + sldl_list.append("") + place_fips_list.append("") + vtd_list.append("") + puma_list.append("") + zcta_list.append("") + + return { + "block_geoid": block_geoids, + "county_fips": county_fips, + "tract_geoid": tract_geoids, + "state_fips": state_fips, + "cbsa_code": cbsa_codes, + "sldu": np.array(sldu_list), + "sldl": np.array(sldl_list), + "place_fips": np.array(place_fips_list), + "vtd": np.array(vtd_list), + "puma": np.array(puma_list), + "zcta": np.array(zcta_list), + "county_index": county_indices, + } + + # === County Filter Functions (for city-level datasets) === diff --git a/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py b/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py index 97c82360..a5ee8ba8 100644 --- a/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py +++ b/policyengine_us_data/datasets/cps/local_area_calibration/calibration_utils.py @@ -548,23 +548,11 @@ def load_cd_geoadj_values( ) rent_lookup[row["cd_geoid"]] = geoadj - # Map each CD to calibrate to its geoadj value - # Handle at-large districts: database uses XX01, rent CSV uses XX00 geoadj_dict = {} for cd in cds_to_calibrate: if cd in rent_lookup: geoadj_dict[cd] = rent_lookup[cd] else: - # Try at-large mapping: XX01 -> XX00 - cd_int = int(cd) - state_fips = cd_int // 100 - district = cd_int % 100 - if district == 1: - at_large_cd = str(state_fips * 100) # XX00 - if at_large_cd in rent_lookup: - geoadj_dict[cd] = rent_lookup[at_large_cd] - continue - # Fallback to national average (geoadj = 1.0) print(f"Warning: No rent data for CD {cd}, using geoadj=1.0") geoadj_dict[cd] = 1.0 diff --git a/policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py b/policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py index 4963f397..bca5f9e4 100644 --- a/policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py +++ b/policyengine_us_data/datasets/cps/local_area_calibration/publish_local_area.py @@ -150,7 +150,7 @@ def build_district_h5( """ cd_int = int(cd_geoid) state_fips = cd_int // 100 - district_num = cd_int % 100 + district_num = max(cd_int % 100, 1) state_code = STATE_CODES.get(state_fips, str(state_fips)) friendly_name = f"{state_code}-{district_num:02d}" @@ -228,7 +228,7 @@ def get_district_friendly_name(cd_geoid: str) -> str: """Convert GEOID to friendly name (e.g., '0101' -> 'AL-01').""" cd_int = int(cd_geoid) state_fips = cd_int // 100 - district_num = cd_int % 100 + district_num = max(cd_int % 100, 1) state_code = STATE_CODES.get(state_fips, str(state_fips)) return f"{state_code}-{district_num:02d}" @@ -327,7 +327,7 @@ def build_and_upload_districts( for i, cd_geoid in enumerate(cds_to_calibrate): cd_int = int(cd_geoid) state_fips = cd_int // 100 - district_num = cd_int % 100 + district_num = max(cd_int % 100, 1) state_code = STATE_CODES.get(state_fips, str(state_fips)) friendly_name = f"{state_code}-{district_num:02d}" diff --git a/policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py b/policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py index 010e151f..0e13f1f0 100644 --- a/policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py +++ b/policyengine_us_data/datasets/cps/local_area_calibration/stacked_dataset_builder.py @@ -25,6 +25,7 @@ ) from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( assign_geography_for_cd, + derive_geography_from_blocks, get_county_filter_probability, get_filtered_block_distribution, ) @@ -67,6 +68,9 @@ def create_sparse_cd_stacked_dataset( dataset_path=None, county_filter=None, seed: int = 42, + rerandomize_takeup: bool = False, + calibration_blocks: np.ndarray = None, + takeup_filter=None, ): """ Create a SPARSE congressional district-stacked dataset using DataFrame approach. @@ -84,6 +88,10 @@ def create_sparse_cd_stacked_dataset( assigned to these counties will be included. Used for city-level datasets. seed: Base random seed for county assignment. Each CD gets seed + int(cd_geoid) for deterministic, order-independent results. Default 42. + calibration_blocks: Optional stacked block GEOID array from calibration. + Shape (n_cds * n_households,) indexed by cds_to_calibrate ordering. + When provided, geography is derived from these blocks instead of + re-drawing, ensuring consistency with calibration matrix. Returns: output_path: Path to the saved .h5 file. @@ -338,13 +346,33 @@ def create_sparse_cd_stacked_dataset( ) # Assign all geography using census block assignment - # For city datasets: use only blocks in target counties - if county_filter is not None: + # When calibration_blocks are provided and no county_filter, + # derive geography from the calibration's block assignments + # to ensure consistency with the calibration matrix. + cal_idx = cds_to_calibrate.index(cd_geoid) + cd_blocks = None + if calibration_blocks is not None and county_filter is None: + cd_blocks = calibration_blocks[ + cal_idx * n_households_orig : (cal_idx + 1) * n_households_orig + ] + has_block = cd_blocks != "" + if has_block.all(): + geography = derive_geography_from_blocks(cd_blocks) + else: + fallback = assign_geography_for_cd( + cd_geoid=cd_geoid, + n_households=n_households_orig, + seed=seed + int(cd_geoid), + ) + cal_geo = derive_geography_from_blocks(cd_blocks[has_block]) + geography = {k: fallback[k].copy() for k in fallback} + for k in cal_geo: + geography[k][has_block] = cal_geo[k] + elif county_filter is not None: filtered_dist = get_filtered_block_distribution( cd_geoid, county_filter ) if not filtered_dist: - # Should not happen if we already checked p_target > 0 continue geography = assign_geography_for_cd( cd_geoid=cd_geoid, @@ -390,6 +418,31 @@ def create_sparse_cd_stacked_dataset( if var != "county": cd_sim.delete_arrays(var) + if rerandomize_takeup: + from policyengine_us_data.utils.takeup import ( + apply_block_takeup_draws_to_sim, + ) + + if cd_blocks is not None: + # Use raw calibration blocks ("" for inactive) so + # entity-per-block counts match the matrix builder + apply_block_takeup_draws_to_sim( + cd_sim, + cd_blocks, + time_period, + takeup_filter=takeup_filter, + ) + else: + apply_block_takeup_draws_to_sim( + cd_sim, + geography["block_geoid"], + time_period, + takeup_filter=takeup_filter, + ) + for var in get_calculated_variables(cd_sim): + if var != "county": + cd_sim.delete_arrays(var) + # Now extract the dataframe - calculated vars will use the updated state df = cd_sim.to_input_dataframe() @@ -786,6 +839,16 @@ def create_sparse_cd_stacked_dataset( type=str, help="State code to process, e.g. RI, CA, NC (only used with --mode single-state)", ) + parser.add_argument( + "--rerandomize-takeup", + action="store_true", + help="Re-randomize takeup draws per CD using geo-salted RNG", + ) + parser.add_argument( + "--calibration-blocks", + default=None, + help="Path to stacked_blocks.npy from calibration", + ) args = parser.parse_args() dataset_path_str = args.dataset_path @@ -814,6 +877,12 @@ def create_sparse_cd_stacked_dataset( f"Weight vector length ({len(w):,}) doesn't match expected ({expected_length:,})" ) + rerand = args.rerandomize_takeup + cal_blocks = None + if args.calibration_blocks: + cal_blocks = np.load(args.calibration_blocks) + print(f"Loaded calibration blocks: {len(cal_blocks):,} entries") + if mode == "national": output_path = f"{output_dir}/national.h5" print(f"\nCreating national dataset with all CDs: {output_path}") @@ -822,6 +891,8 @@ def create_sparse_cd_stacked_dataset( cds_to_calibrate, dataset_path=dataset_path_str, output_path=output_path, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) elif mode == "states": @@ -839,6 +910,8 @@ def create_sparse_cd_stacked_dataset( cd_subset=cd_subset, dataset_path=dataset_path_str, output_path=output_path, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) elif mode == "cds": @@ -846,7 +919,7 @@ def create_sparse_cd_stacked_dataset( # Convert GEOID to friendly name: 3705 -> NC-05 cd_int = int(cd_geoid) state_fips = cd_int // 100 - district_num = cd_int % 100 + district_num = max(cd_int % 100, 1) state_code = STATE_CODES.get(state_fips, str(state_fips)) friendly_name = f"{state_code}-{district_num:02d}" @@ -860,6 +933,8 @@ def create_sparse_cd_stacked_dataset( cd_subset=[cd_geoid], dataset_path=dataset_path_str, output_path=output_path, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) elif mode == "single-cd": @@ -875,6 +950,8 @@ def create_sparse_cd_stacked_dataset( cd_subset=[args.cd], dataset_path=dataset_path_str, output_path=output_path, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) elif mode == "single-state": @@ -906,6 +983,8 @@ def create_sparse_cd_stacked_dataset( cd_subset=cd_subset, dataset_path=dataset_path_str, output_path=output_path, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) elif mode == "nyc": @@ -927,6 +1006,8 @@ def create_sparse_cd_stacked_dataset( dataset_path=dataset_path_str, output_path=output_path, county_filter=NYC_COUNTIES, + rerandomize_takeup=rerand, + calibration_blocks=cal_blocks, ) print("\nDone!") diff --git a/policyengine_us_data/db/create_initial_strata.py b/policyengine_us_data/db/create_initial_strata.py index 0b9ae8a6..253262c9 100644 --- a/policyengine_us_data/db/create_initial_strata.py +++ b/policyengine_us_data/db/create_initial_strata.py @@ -40,8 +40,9 @@ def fetch_congressional_districts(year): df["state_fips"] = df["state"].astype(int) df = df[df["state_fips"] <= 56].copy() df["district_number"] = df["congressional district"].apply( - lambda x: 0 if x in ["ZZ", "98"] else int(x) + lambda x: int(x) if x not in ["ZZ"] else -1 ) + df = df[df["district_number"] >= 0].copy() # Filter out statewide summary records for multi-district states df["n_districts"] = df.groupby("state_fips")["state_fips"].transform( @@ -49,8 +50,6 @@ def fetch_congressional_districts(year): ) df = df[(df["n_districts"] == 1) | (df["district_number"] > 0)].copy() df = df.drop(columns=["n_districts"]) - - df.loc[df["district_number"] == 0, "district_number"] = 1 df["congressional_district_geoid"] = ( df["state_fips"] * 100 + df["district_number"] ) diff --git a/policyengine_us_data/tests/test_calibration/test_clone_and_assign.py b/policyengine_us_data/tests/test_calibration/test_clone_and_assign.py index 0ba33054..b2d45bd5 100644 --- a/policyengine_us_data/tests/test_calibration/test_clone_and_assign.py +++ b/policyengine_us_data/tests/test_calibration/test_clone_and_assign.py @@ -133,6 +133,22 @@ def test_state_from_block(self, mock_load): expected = int(r.block_geoid[i][:2]) assert r.state_fips[i] == expected + @patch( + "policyengine_us_data.calibration.clone_and_assign" + ".load_global_block_distribution" + ) + def test_no_cd_collisions_across_clones(self, mock_load): + mock_load.return_value = _mock_distribution() + r = assign_random_geography(n_records=100, n_clones=3, seed=42) + for rec in range(r.n_records): + rec_cds = [ + r.cd_geoid[clone * r.n_records + rec] + for clone in range(r.n_clones) + ] + assert len(rec_cds) == len( + set(rec_cds) + ), f"Record {rec} has duplicate CDs: {rec_cds}" + def test_missing_file_raises(self, tmp_path): fake = tmp_path / "nonexistent" fake.mkdir() @@ -150,6 +166,7 @@ def test_doubles_n_records(self): geo = GeographyAssignment( block_geoid=np.array(["010010001001001", "020010001001001"] * 3), cd_geoid=np.array(["101", "202"] * 3), + county_fips=np.array(["01001", "02001"] * 3), state_fips=np.array([1, 2] * 3), n_records=2, n_clones=3, @@ -172,6 +189,9 @@ def test_puf_half_matches_cps_half(self): ] ), cd_geoid=np.array(["101", "202", "1036", "653", "4831", "1227"]), + county_fips=np.array( + ["01001", "02001", "36010", "06010", "48010", "12010"] + ), state_fips=np.array([1, 2, 36, 6, 48, 12]), n_records=3, n_clones=2, diff --git a/policyengine_us_data/tests/test_calibration/test_target_config.py b/policyengine_us_data/tests/test_calibration/test_target_config.py new file mode 100644 index 00000000..9241660c --- /dev/null +++ b/policyengine_us_data/tests/test_calibration/test_target_config.py @@ -0,0 +1,177 @@ +"""Tests for target config filtering in unified calibration.""" + +import numpy as np +import pandas as pd +import pytest +from scipy import sparse + +from policyengine_us_data.calibration.unified_calibration import ( + apply_target_config, + load_target_config, + save_calibration_package, + load_calibration_package, +) + + +@pytest.fixture +def sample_targets(): + targets_df = pd.DataFrame( + { + "variable": [ + "snap", + "snap", + "eitc", + "eitc", + "rent", + "person_count", + ], + "geo_level": [ + "national", + "state", + "district", + "state", + "national", + "national", + ], + "domain_variable": [ + "snap", + "snap", + "eitc", + "eitc", + "rent", + "person_count", + ], + "geographic_id": ["US", "6", "0601", "6", "US", "US"], + "value": [1000, 500, 200, 300, 800, 5000], + } + ) + n_rows = len(targets_df) + n_cols = 10 + rng = np.random.default_rng(42) + X = sparse.random(n_rows, n_cols, density=0.5, random_state=rng) + X = X.tocsr() + target_names = [ + f"{r.variable}_{r.geo_level}_{r.geographic_id}" + for _, r in targets_df.iterrows() + ] + return targets_df, X, target_names + + +class TestApplyTargetConfig: + def test_empty_config_keeps_all(self, sample_targets): + df, X, names = sample_targets + config = {"exclude": []} + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert len(out_df) == len(df) + assert out_X.shape == X.shape + assert out_names == names + + def test_single_variable_geo_exclusion(self, sample_targets): + df, X, names = sample_targets + config = {"exclude": [{"variable": "rent", "geo_level": "national"}]} + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert len(out_df) == len(df) - 1 + assert "rent" not in out_df["variable"].values + + def test_multiple_exclusions(self, sample_targets): + df, X, names = sample_targets + config = { + "exclude": [ + {"variable": "rent", "geo_level": "national"}, + {"variable": "eitc", "geo_level": "district"}, + ] + } + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert len(out_df) == len(df) - 2 + kept = set(zip(out_df["variable"], out_df["geo_level"])) + assert ("rent", "national") not in kept + assert ("eitc", "district") not in kept + assert ("eitc", "state") in kept + + def test_domain_variable_matching(self, sample_targets): + df, X, names = sample_targets + config = { + "exclude": [ + { + "variable": "snap", + "geo_level": "national", + "domain_variable": "snap", + } + ] + } + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert len(out_df) == len(df) - 1 + + def test_matrix_and_names_stay_in_sync(self, sample_targets): + df, X, names = sample_targets + config = { + "exclude": [{"variable": "person_count", "geo_level": "national"}] + } + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert out_X.shape[0] == len(out_df) + assert len(out_names) == len(out_df) + assert out_X.shape[1] == X.shape[1] + + def test_no_match_keeps_all(self, sample_targets): + df, X, names = sample_targets + config = { + "exclude": [{"variable": "nonexistent", "geo_level": "national"}] + } + out_df, out_X, out_names = apply_target_config(df, X, names, config) + assert len(out_df) == len(df) + assert out_X.shape[0] == X.shape[0] + + +class TestLoadTargetConfig: + def test_load_valid_config(self, tmp_path): + config_file = tmp_path / "config.yaml" + config_file.write_text( + "exclude:\n" " - variable: snap\n" " geo_level: national\n" + ) + config = load_target_config(str(config_file)) + assert len(config["exclude"]) == 1 + assert config["exclude"][0]["variable"] == "snap" + + def test_load_empty_config(self, tmp_path): + config_file = tmp_path / "empty.yaml" + config_file.write_text("") + config = load_target_config(str(config_file)) + assert config["exclude"] == [] + + +class TestCalibrationPackageRoundTrip: + def test_round_trip(self, sample_targets, tmp_path): + df, X, names = sample_targets + pkg_path = str(tmp_path / "package.pkl") + metadata = { + "dataset_path": "/tmp/test.h5", + "db_path": "/tmp/test.db", + "n_clones": 5, + "n_records": X.shape[1], + "seed": 42, + "created_at": "2024-01-01T00:00:00", + "target_config": None, + } + save_calibration_package(pkg_path, X, df, names, metadata) + loaded = load_calibration_package(pkg_path) + + assert loaded["target_names"] == names + pd.testing.assert_frame_equal(loaded["targets_df"], df) + assert loaded["X_sparse"].shape == X.shape + assert loaded["metadata"]["seed"] == 42 + + def test_package_then_filter(self, sample_targets, tmp_path): + df, X, names = sample_targets + pkg_path = str(tmp_path / "package.pkl") + metadata = {"n_records": X.shape[1]} + save_calibration_package(pkg_path, X, df, names, metadata) + loaded = load_calibration_package(pkg_path) + + config = {"exclude": [{"variable": "rent", "geo_level": "national"}]} + out_df, out_X, out_names = apply_target_config( + loaded["targets_df"], + loaded["X_sparse"], + loaded["target_names"], + config, + ) + assert len(out_df) == len(df) - 1 diff --git a/policyengine_us_data/tests/test_calibration/test_unified_calibration.py b/policyengine_us_data/tests/test_calibration/test_unified_calibration.py index 2d3f8061..af262828 100644 --- a/policyengine_us_data/tests/test_calibration/test_unified_calibration.py +++ b/policyengine_us_data/tests/test_calibration/test_unified_calibration.py @@ -1,13 +1,25 @@ -"""Tests for unified_calibration module. +"""Tests for unified_calibration and shared takeup module. -Focuses on rerandomize_takeup: verifies draws differ by -block and are reproducible within the same block. +Verifies geo-salted draws are reproducible and vary by geo_id, +SIMPLE_TAKEUP_VARS / TAKEUP_AFFECTED_TARGETS configs are valid, +block-level takeup seeding, county precomputation, and CLI flags. """ import numpy as np import pytest from policyengine_us_data.utils.randomness import seeded_rng +from policyengine_us_data.utils.takeup import ( + SIMPLE_TAKEUP_VARS, + TAKEUP_AFFECTED_TARGETS, + draw_takeup_for_geo, + compute_entity_takeup_for_geo, + compute_block_takeup_for_entities, + _resolve_rate, +) +from policyengine_us_data.calibration.clone_and_assign import ( + GeographyAssignment, +) class TestRerandomizeTakeupSeeding: @@ -61,14 +73,90 @@ def test_rate_comparison_produces_booleans(self): assert 0.70 < frac < 0.80 +class TestGeoSaltedDraws: + """Verify draw_takeup_for_geo produces reproducible, + geo-dependent draws using geo: salt prefix.""" + + def test_same_geo_same_draws(self): + d1 = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 500) + d2 = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 500) + np.testing.assert_array_equal(d1, d2) + + def test_different_geos_different_draws(self): + d1 = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 500) + d2 = draw_takeup_for_geo("takes_up_snap_if_eligible", "4816", 500) + assert not np.array_equal(d1, d2) + + def test_different_vars_different_draws(self): + d1 = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 500) + d2 = draw_takeup_for_geo("takes_up_aca_if_eligible", "3701", 500) + assert not np.array_equal(d1, d2) + + def test_geo_salt_not_collide_with_block_salt(self): + d_geo = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 500) + rng_block = seeded_rng("takes_up_snap_if_eligible", salt="3701") + d_block = rng_block.random(500) + assert not np.array_equal(d_geo, d_block) + + def test_draws_in_unit_interval(self): + draws = draw_takeup_for_geo("takes_up_snap_if_eligible", "3701", 10000) + assert draws.min() >= 0.0 + assert draws.max() < 1.0 + + +class TestComputeEntityTakeup: + """Verify compute_entity_takeup_for_geo returns + correct boolean arrays.""" + + def test_returns_all_takeup_vars(self): + n = {"person": 100, "tax_unit": 50, "spm_unit": 40} + result = compute_entity_takeup_for_geo("3701", n, 37, 2024) + for spec in SIMPLE_TAKEUP_VARS: + assert spec["variable"] in result + assert result[spec["variable"]].dtype == bool + + def test_correct_entity_counts(self): + n = {"person": 200, "tax_unit": 80, "spm_unit": 60} + result = compute_entity_takeup_for_geo("3701", n, 37, 2024) + assert len(result["takes_up_snap_if_eligible"]) == 60 + assert len(result["takes_up_aca_if_eligible"]) == 80 + assert len(result["takes_up_ssi_if_eligible"]) == 200 + + def test_reproducible(self): + n = {"person": 100, "tax_unit": 50, "spm_unit": 40} + r1 = compute_entity_takeup_for_geo("3701", n, 37, 2024) + r2 = compute_entity_takeup_for_geo("3701", n, 37, 2024) + for var in r1: + np.testing.assert_array_equal(r1[var], r2[var]) + + def test_different_geo_different_result(self): + n = {"person": 100, "tax_unit": 50, "spm_unit": 40} + r1 = compute_entity_takeup_for_geo("3701", n, 37, 2024) + r2 = compute_entity_takeup_for_geo("4816", n, 48, 2024) + differs = any(not np.array_equal(r1[v], r2[v]) for v in r1) + assert differs + + +class TestResolveRate: + """Verify _resolve_rate handles scalar and dict rates.""" + + def test_scalar_rate(self): + assert _resolve_rate(0.82, 37) == 0.82 + + def test_state_dict_rate(self): + rates = {"NC": 0.94, "TX": 0.76} + assert _resolve_rate(rates, 37) == 0.94 + assert _resolve_rate(rates, 48) == 0.76 + + def test_unknown_state_fallback(self): + rates = {"NC": 0.94} + assert _resolve_rate(rates, 99) == 0.8 + + class TestSimpleTakeupConfig: """Verify the SIMPLE_TAKEUP_VARS config is well-formed.""" def test_all_entries_have_required_keys(self): - from policyengine_us_data.calibration.unified_calibration import ( - SIMPLE_TAKEUP_VARS, - ) - for entry in SIMPLE_TAKEUP_VARS: assert "variable" in entry assert "entity" in entry @@ -80,8 +168,520 @@ def test_all_entries_have_required_keys(self): ) def test_expected_count(self): + assert len(SIMPLE_TAKEUP_VARS) == 9 + + def test_importable_from_unified_calibration(self): + from policyengine_us_data.calibration.unified_calibration import ( + SIMPLE_TAKEUP_VARS as UC_VARS, + ) + + assert UC_VARS is SIMPLE_TAKEUP_VARS + + +class TestTakeupAffectedTargets: + """Verify TAKEUP_AFFECTED_TARGETS is consistent.""" + + def test_all_entries_have_required_keys(self): + for key, info in TAKEUP_AFFECTED_TARGETS.items(): + assert "takeup_var" in info + assert "entity" in info + assert "rate_key" in info + assert info["entity"] in ( + "person", + "tax_unit", + "spm_unit", + ) + + def test_takeup_vars_exist_in_simple_vars(self): + simple_var_names = {s["variable"] for s in SIMPLE_TAKEUP_VARS} + for info in TAKEUP_AFFECTED_TARGETS.values(): + assert info["takeup_var"] in simple_var_names + + +class TestParseArgsNewFlags: + """Verify new CLI flags are parsed correctly.""" + + def test_target_config_flag(self): + from policyengine_us_data.calibration.unified_calibration import ( + parse_args, + ) + + args = parse_args(["--target-config", "config.yaml"]) + assert args.target_config == "config.yaml" + + def test_build_only_flag(self): + from policyengine_us_data.calibration.unified_calibration import ( + parse_args, + ) + + args = parse_args(["--build-only"]) + assert args.build_only is True + + def test_package_path_flag(self): + from policyengine_us_data.calibration.unified_calibration import ( + parse_args, + ) + + args = parse_args(["--package-path", "pkg.pkl"]) + assert args.package_path == "pkg.pkl" + + def test_hyperparams_flags(self): + from policyengine_us_data.calibration.unified_calibration import ( + parse_args, + ) + + args = parse_args( + [ + "--beta", + "0.65", + "--lambda-l2", + "1e-8", + "--learning-rate", + "0.2", + ] + ) + assert args.beta == 0.65 + assert args.lambda_l2 == 1e-8 + assert args.learning_rate == 0.2 + + def test_hyperparams_defaults(self): + from policyengine_us_data.calibration.unified_calibration import ( + BETA, + LAMBDA_L2, + LEARNING_RATE, + parse_args, + ) + + args = parse_args([]) + assert args.beta == BETA + assert args.lambda_l2 == LAMBDA_L2 + assert args.learning_rate == LEARNING_RATE + + def test_skip_takeup_rerandomize_flag(self): + from policyengine_us_data.calibration.unified_calibration import ( + parse_args, + ) + + args = parse_args(["--skip-takeup-rerandomize"]) + assert args.skip_takeup_rerandomize is True + + args_default = parse_args([]) + assert args_default.skip_takeup_rerandomize is False + + +class TestGeographyAssignmentCountyFips: + """Verify county_fips field on GeographyAssignment.""" + + def test_county_fips_equals_block_prefix(self): + blocks = np.array( + ["370010001001001", "480010002002002", "060370003003003"] + ) + ga = GeographyAssignment( + block_geoid=blocks, + cd_geoid=np.array(["3701", "4801", "0613"]), + county_fips=np.array([b[:5] for b in blocks]), + state_fips=np.array([37, 48, 6]), + n_records=3, + n_clones=1, + ) + expected = np.array(["37001", "48001", "06037"]) + np.testing.assert_array_equal(ga.county_fips, expected) + + def test_county_fips_length(self): + blocks = np.array(["370010001001001"] * 5) + counties = np.array([b[:5] for b in blocks]) + ga = GeographyAssignment( + block_geoid=blocks, + cd_geoid=np.array(["3701"] * 5), + county_fips=counties, + state_fips=np.array([37] * 5), + n_records=5, + n_clones=1, + ) + assert len(ga.county_fips) == 5 + assert all(len(c) == 5 for c in ga.county_fips) + + +class TestBlockTakeupSeeding: + """Verify compute_block_takeup_for_entities is + reproducible and block-dependent.""" + + def test_reproducible(self): + blocks = np.array(["010010001001001"] * 50 + ["020010001001001"] * 50) + states = np.array([1] * 50 + [2] * 50) + r1 = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.8, blocks, states + ) + r2 = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.8, blocks, states + ) + np.testing.assert_array_equal(r1, r2) + + def test_different_blocks_different_draws(self): + n = 500 + blocks_a = np.array(["010010001001001"] * n) + blocks_b = np.array(["020010001001001"] * n) + states = np.array([1] * n) + r_a = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.8, blocks_a, states + ) + r_b = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.8, blocks_b, states + ) + assert not np.array_equal(r_a, r_b) + + def test_returns_booleans(self): + blocks = np.array(["370010001001001"] * 100) + states = np.array([37] * 100) + result = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.8, blocks, states + ) + assert result.dtype == bool + + def test_rate_respected(self): + n = 10000 + blocks = np.array(["370010001001001"] * n) + states = np.array([37] * n) + result = compute_block_takeup_for_entities( + "takes_up_snap_if_eligible", 0.75, blocks, states + ) + frac = result.mean() + assert 0.70 < frac < 0.80 + + +class TestAssembleCloneValuesCounty: + """Verify _assemble_clone_values merges state and + county values correctly.""" + + def test_county_var_uses_county_values(self): + from policyengine_us_data.calibration.unified_matrix_builder import ( + UnifiedMatrixBuilder, + ) + + n = 4 + state_values = { + 1: { + "hh": { + "aca_ptc": np.array([100] * n, dtype=np.float32), + }, + "person": {}, + "entity": {}, + }, + 2: { + "hh": { + "aca_ptc": np.array([200] * n, dtype=np.float32), + }, + "person": {}, + "entity": {}, + }, + } + county_values = { + "01001": { + "hh": { + "aca_ptc": np.array([111] * n, dtype=np.float32), + }, + "entity": {}, + }, + "02001": { + "hh": { + "aca_ptc": np.array([222] * n, dtype=np.float32), + }, + "entity": {}, + }, + } + clone_states = np.array([1, 1, 2, 2]) + clone_counties = np.array(["01001", "01001", "02001", "02001"]) + person_hh_idx = np.array([0, 1, 2, 3]) + + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + hh_vars, _ = builder._assemble_clone_values( + state_values, + clone_states, + person_hh_idx, + {"aca_ptc"}, + set(), + county_values=county_values, + clone_counties=clone_counties, + county_dependent_vars={"aca_ptc"}, + ) + expected = np.array([111, 111, 222, 222], dtype=np.float32) + np.testing.assert_array_equal(hh_vars["aca_ptc"], expected) + + def test_non_county_var_uses_state_values(self): + from policyengine_us_data.calibration.unified_matrix_builder import ( + UnifiedMatrixBuilder, + ) + + n = 4 + state_values = { + 1: { + "hh": { + "snap": np.array([50] * n, dtype=np.float32), + }, + "person": {}, + "entity": {}, + }, + 2: { + "hh": { + "snap": np.array([60] * n, dtype=np.float32), + }, + "person": {}, + "entity": {}, + }, + } + clone_states = np.array([1, 1, 2, 2]) + clone_counties = np.array(["01001", "01001", "02001", "02001"]) + person_hh_idx = np.array([0, 1, 2, 3]) + + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + hh_vars, _ = builder._assemble_clone_values( + state_values, + clone_states, + person_hh_idx, + {"snap"}, + set(), + county_values={}, + clone_counties=clone_counties, + county_dependent_vars={"aca_ptc"}, + ) + expected = np.array([50, 50, 60, 60], dtype=np.float32) + np.testing.assert_array_equal(hh_vars["snap"], expected) + + +class TestConvertBlocksToStackedFormat: + """Verify convert_blocks_to_stacked_format produces + correct stacked block arrays.""" + + def test_basic_conversion(self): + from policyengine_us_data.calibration.unified_calibration import ( + convert_blocks_to_stacked_format, + ) + + block_geoid = np.array( + [ + "370010001001001", + "370010001001002", + "480010002002001", + "480010002002002", + ] + ) + cd_geoid = np.array(["3701", "3701", "4801", "4801"]) + base_n_records = 2 + cds_ordered = ["3701", "4801"] + + result = convert_blocks_to_stacked_format( + block_geoid, cd_geoid, base_n_records, cds_ordered + ) + assert result.dtype.kind == "U" + assert len(result) == 4 + assert result[0] == "370010001001001" + assert result[1] == "370010001001002" + assert result[2] == "480010002002001" + assert result[3] == "480010002002002" + + def test_empty_slots(self): + from policyengine_us_data.calibration.unified_calibration import ( + convert_blocks_to_stacked_format, + ) + + block_geoid = np.array(["370010001001001", "370010001001002"]) + cd_geoid = np.array(["3701", "3701"]) + base_n_records = 2 + cds_ordered = ["3701", "4801"] + + result = convert_blocks_to_stacked_format( + block_geoid, cd_geoid, base_n_records, cds_ordered + ) + assert len(result) == 4 + assert result[0] == "370010001001001" + assert result[1] == "370010001001002" + assert result[2] == "" + assert result[3] == "" + + def test_first_clone_wins(self): from policyengine_us_data.calibration.unified_calibration import ( - SIMPLE_TAKEUP_VARS, + convert_blocks_to_stacked_format, + ) + + block_geoid = np.array( + [ + "370010001001001", + "370010001001002", + "370010001001099", + "370010001001099", + ] + ) + cd_geoid = np.array(["3701", "3701", "3701", "3701"]) + base_n_records = 2 + cds_ordered = ["3701"] + + result = convert_blocks_to_stacked_format( + block_geoid, cd_geoid, base_n_records, cds_ordered + ) + assert result[0] == "370010001001001" + assert result[1] == "370010001001002" + + +class TestTakeupDrawConsistency: + """Verify the matrix builder's inline takeup loop and + compute_block_takeup_for_entities produce identical draws + when given the same (block, household) inputs.""" + + def test_matrix_and_stacked_identical_draws(self): + """Both paths must produce identical boolean arrays.""" + var = "takes_up_snap_if_eligible" + rate = 0.75 + + # 2 blocks, 3 households, variable entity counts per HH + # HH0 has 2 entities in block A + # HH1 has 3 entities in block A + # HH2 has 1 entity in block B + blocks = np.array( + [ + "370010001001001", + "370010001001001", + "370010001001001", + "370010001001001", + "370010001001001", + "480010002002002", + ] + ) + hh_ids = np.array([100, 100, 200, 200, 200, 300]) + states = np.array([37, 37, 37, 37, 37, 48]) + + # Path 1: compute_block_takeup_for_entities (stacked) + stacked = compute_block_takeup_for_entities( + var, rate, blocks, states, hh_ids + ) + + # Path 2: reproduce matrix builder inline logic + n = len(blocks) + inline_takeup = np.zeros(n, dtype=bool) + for blk in np.unique(blocks): + bm = blocks == blk + for hh_id in np.unique(hh_ids[bm]): + hh_mask = bm & (hh_ids == hh_id) + rng = seeded_rng(var, salt=f"{blk}:{int(hh_id)}") + draws = rng.random(int(hh_mask.sum())) + inline_takeup[hh_mask] = draws < rate + + np.testing.assert_array_equal(stacked, inline_takeup) + + def test_aggregation_entity_to_household(self): + """np.add.at aggregation matches manual per-HH sum.""" + n_hh = 3 + n_ent = 6 + ent_hh = np.array([0, 0, 1, 1, 1, 2]) + eligible = np.array( + [100.0, 200.0, 50.0, 150.0, 100.0, 300.0], + dtype=np.float32, + ) + takeup = np.array([True, False, True, True, False, True]) + + ent_values = (eligible * takeup).astype(np.float32) + hh_result = np.zeros(n_hh, dtype=np.float32) + np.add.at(hh_result, ent_hh, ent_values) + + # Manual: HH0=100, HH1=50+150=200, HH2=300 + expected = np.array([100.0, 200.0, 300.0], dtype=np.float32) + np.testing.assert_array_equal(hh_result, expected) + + def test_state_specific_rate_resolved_from_block(self): + """Dict rates are resolved per block's state FIPS.""" + from policyengine_us_data.utils.takeup import _resolve_rate + + var = "takes_up_snap_if_eligible" + rate_dict = {"NC": 0.9, "TX": 0.6} + n = 5000 + + blocks_nc = np.array(["370010001001001"] * n) + states_nc = np.array([37] * n) + result_nc = compute_block_takeup_for_entities( + var, rate_dict, blocks_nc, states_nc + ) + # NC rate=0.9, expect ~90% + frac_nc = result_nc.mean() + assert 0.85 < frac_nc < 0.95, f"NC frac={frac_nc}" + + blocks_tx = np.array(["480010002002002"] * n) + states_tx = np.array([48] * n) + result_tx = compute_block_takeup_for_entities( + var, rate_dict, blocks_tx, states_tx + ) + # TX rate=0.6, expect ~60% + frac_tx = result_tx.mean() + assert 0.55 < frac_tx < 0.65, f"TX frac={frac_tx}" + + # Verify _resolve_rate actually gives different rates + assert _resolve_rate(rate_dict, 37) == 0.9 + assert _resolve_rate(rate_dict, 48) == 0.6 + + +class TestDeriveGeographyFromBlocks: + """Verify derive_geography_from_blocks returns correct + geography dict from pre-assigned blocks.""" + + def test_returns_expected_keys(self): + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + derive_geography_from_blocks, + ) + + blocks = np.array(["370010001001001"]) + result = derive_geography_from_blocks(blocks) + expected_keys = { + "block_geoid", + "county_fips", + "tract_geoid", + "state_fips", + "cbsa_code", + "sldu", + "sldl", + "place_fips", + "vtd", + "puma", + "zcta", + "county_index", + } + assert set(result.keys()) == expected_keys + + def test_county_fips_derived(self): + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + derive_geography_from_blocks, + ) + + blocks = np.array(["370010001001001", "480010002002002"]) + result = derive_geography_from_blocks(blocks) + np.testing.assert_array_equal( + result["county_fips"], + np.array(["37001", "48001"]), + ) + + def test_state_fips_derived(self): + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + derive_geography_from_blocks, + ) + + blocks = np.array(["370010001001001", "060370003003003"]) + result = derive_geography_from_blocks(blocks) + np.testing.assert_array_equal( + result["state_fips"], + np.array(["37", "06"]), + ) + + def test_tract_geoid_derived(self): + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + derive_geography_from_blocks, + ) + + blocks = np.array(["370010001001001"]) + result = derive_geography_from_blocks(blocks) + assert result["tract_geoid"][0] == "37001000100" + + def test_block_geoid_passthrough(self): + from policyengine_us_data.datasets.cps.local_area_calibration.block_assignment import ( + derive_geography_from_blocks, ) - assert len(SIMPLE_TAKEUP_VARS) == 8 + blocks = np.array(["370010001001001"]) + result = derive_geography_from_blocks(blocks) + assert result["block_geoid"][0] == "370010001001001" diff --git a/policyengine_us_data/tests/test_calibration/test_unified_matrix_builder.py b/policyengine_us_data/tests/test_calibration/test_unified_matrix_builder.py index ea2d49c5..1a312e99 100644 --- a/policyengine_us_data/tests/test_calibration/test_unified_matrix_builder.py +++ b/policyengine_us_data/tests/test_calibration/test_unified_matrix_builder.py @@ -395,5 +395,692 @@ def test_endswith_count(self): ) +class _FakeArrayResult: + """Minimal stand-in for sim.calculate() return values.""" + + def __init__(self, values): + self.values = values + + +class _FakeSimulation: + """Lightweight mock for policyengine_us.Microsimulation. + + Tracks set_input and delete_arrays calls, returns + configurable arrays from calculate(). + """ + + def __init__(self, n_hh=4, n_person=8, n_tax_unit=4, n_spm_unit=4): + self.n_hh = n_hh + self.n_person = n_person + self.n_tax_unit = n_tax_unit + self.n_spm_unit = n_spm_unit + + self.set_input_calls = [] + self.delete_arrays_calls = [] + self.calculate_calls = [] + + # Configurable return values for calculate() + self._calc_returns = {} + + def set_input(self, var, period, values): + self.set_input_calls.append((var, period, values)) + + def delete_arrays(self, var): + self.delete_arrays_calls.append(var) + + def calculate(self, var, period=None, map_to=None): + self.calculate_calls.append((var, period, map_to)) + if var in self._calc_returns: + return _FakeArrayResult(self._calc_returns[var]) + # Default arrays by entity/map_to + if var.endswith("_id"): + entity = var.replace("_id", "") + sizes = { + "household": self.n_hh, + "person": self.n_person, + "tax_unit": self.n_tax_unit, + "spm_unit": self.n_spm_unit, + } + n = sizes.get(entity, self.n_hh) + return _FakeArrayResult(np.arange(n)) + if map_to == "household": + return _FakeArrayResult(np.ones(self.n_hh, dtype=np.float32)) + if map_to == "person": + return _FakeArrayResult(np.ones(self.n_person, dtype=np.float32)) + # entity-level (spm_unit, tax_unit, person) + sizes = { + "spm_unit": self.n_spm_unit, + "tax_unit": self.n_tax_unit, + "person": self.n_person, + } + n = sizes.get(map_to, self.n_hh) + return _FakeArrayResult(np.ones(n, dtype=np.float32)) + + +import numpy as np +from unittest.mock import patch, MagicMock +from collections import namedtuple + +_FakeGeo = namedtuple( + "FakeGeo", + ["state_fips", "n_records", "county_fips", "block_geoid"], +) + + +class TestBuildStateValues(unittest.TestCase): + """Test _build_state_values orchestration logic.""" + + def _make_builder(self): + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + builder.time_period = 2024 + builder.dataset_path = "fake.h5" + return builder + + def _make_geo(self, states, n_records=4): + return _FakeGeo( + state_fips=np.array(states), + n_records=n_records, + county_fips=np.array(["00000"] * len(states)), + block_geoid=np.array(["000000000000000"] * len(states)), + ) + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=["var_a"], + ) + @patch("policyengine_us.Microsimulation") + def test_return_structure_no_takeup(self, mock_msim_cls, mock_gcv): + sim1 = _FakeSimulation() + sim2 = _FakeSimulation() + mock_msim_cls.side_effect = [sim1, sim2] + + builder = self._make_builder() + geo = self._make_geo([37, 48]) + + result = builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars={"income"}, + geography=geo, + rerandomize_takeup=False, + ) + # Both states present + assert 37 in result + assert 48 in result + # Each has hh/person/entity + for st in (37, 48): + assert "hh" in result[st] + assert "person" in result[st] + assert "entity" in result[st] + # entity is empty when not rerandomizing + assert result[st]["entity"] == {} + # hh values are float32 + assert result[st]["hh"]["snap"].dtype == np.float32 + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_fresh_sim_per_state(self, mock_msim_cls, mock_gcv): + mock_msim_cls.side_effect = [ + _FakeSimulation(), + _FakeSimulation(), + ] + builder = self._make_builder() + geo = self._make_geo([37, 48]) + + builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=False, + ) + assert mock_msim_cls.call_count == 2 + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_state_fips_set_correctly(self, mock_msim_cls, mock_gcv): + sims = [_FakeSimulation(), _FakeSimulation()] + mock_msim_cls.side_effect = sims + + builder = self._make_builder() + geo = self._make_geo([37, 48]) + + builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=False, + ) + + # First sim should get state 37 + fips_calls_0 = [ + c for c in sims[0].set_input_calls if c[0] == "state_fips" + ] + assert len(fips_calls_0) == 1 + np.testing.assert_array_equal( + fips_calls_0[0][2], np.full(4, 37, dtype=np.int32) + ) + + # Second sim should get state 48 + fips_calls_1 = [ + c for c in sims[1].set_input_calls if c[0] == "state_fips" + ] + assert len(fips_calls_1) == 1 + np.testing.assert_array_equal( + fips_calls_1[0][2], np.full(4, 48, dtype=np.int32) + ) + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_takeup_vars_forced_true(self, mock_msim_cls, mock_gcv): + sim = _FakeSimulation() + mock_msim_cls.return_value = sim + + builder = self._make_builder() + geo = self._make_geo([37]) + + builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=True, + ) + + from policyengine_us_data.utils.takeup import ( + SIMPLE_TAKEUP_VARS, + ) + + takeup_var_names = {s["variable"] for s in SIMPLE_TAKEUP_VARS} + + # Check that every SIMPLE_TAKEUP_VAR was set to ones + set_true_vars = set() + for var, period, values in sim.set_input_calls: + if var in takeup_var_names: + assert values.dtype == bool + assert values.all(), f"{var} not forced True" + set_true_vars.add(var) + + assert takeup_var_names == set_true_vars, ( + f"Missing forced-true vars: " f"{takeup_var_names - set_true_vars}" + ) + + # Entity-level calculation happens for affected target + entity_calcs = [ + c + for c in sim.calculate_calls + if c[0] == "snap" and c[2] not in ("household", "person", None) + ] + assert len(entity_calcs) >= 1 + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_count_vars_skipped(self, mock_msim_cls, mock_gcv): + sim = _FakeSimulation() + mock_msim_cls.return_value = sim + + builder = self._make_builder() + geo = self._make_geo([37]) + + builder._build_state_values( + sim=None, + target_vars={"snap", "snap_count"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=False, + ) + + # snap calculated, snap_count NOT calculated + calc_vars = [c[0] for c in sim.calculate_calls] + assert "snap" in calc_vars + assert "snap_count" not in calc_vars + + +class TestBuildCountyValues(unittest.TestCase): + """Test _build_county_values orchestration logic.""" + + def _make_builder(self): + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + builder.time_period = 2024 + builder.dataset_path = "fake.h5" + return builder + + def _make_geo(self, county_fips_list, n_records=4): + states = [int(c[:2]) for c in county_fips_list] + return _FakeGeo( + state_fips=np.array(states), + n_records=n_records, + county_fips=np.array(county_fips_list), + block_geoid=np.array(["000000000000000"] * len(county_fips_list)), + ) + + def test_returns_empty_when_county_level_false(self): + builder = self._make_builder() + geo = self._make_geo(["37001"]) + result = builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=False, + ) + assert result == {} + + def test_returns_empty_when_no_targets(self): + builder = self._make_builder() + geo = self._make_geo(["37001"]) + result = builder._build_county_values( + sim=None, + county_dep_targets=set(), + geography=geo, + rerandomize_takeup=False, + county_level=True, + ) + assert result == {} + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=["var_a"], + ) + @patch("policyengine_us.Microsimulation") + def test_return_structure(self, mock_msim_cls, mock_gcv, mock_county_idx): + sim = _FakeSimulation() + mock_msim_cls.return_value = sim + + builder = self._make_builder() + geo = self._make_geo(["37001", "37002"]) + + result = builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + ) + assert "37001" in result + assert "37002" in result + for cfips in ("37001", "37002"): + assert "hh" in result[cfips] + assert "entity" in result[cfips] + # No person-level in county values + assert "person" not in result[cfips] + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=["var_a"], + ) + @patch("policyengine_us.Microsimulation") + def test_sim_reuse_within_state( + self, mock_msim_cls, mock_gcv, mock_county_idx + ): + sim = _FakeSimulation() + mock_msim_cls.return_value = sim + + builder = self._make_builder() + geo = self._make_geo(["37001", "37002"]) + + builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + ) + # 1 state -> 1 Microsimulation + assert mock_msim_cls.call_count == 1 + # 2 counties -> county set_input called twice + county_calls = [c for c in sim.set_input_calls if c[0] == "county"] + assert len(county_calls) == 2 + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_fresh_sim_across_states( + self, mock_msim_cls, mock_gcv, mock_county_idx + ): + mock_msim_cls.side_effect = [ + _FakeSimulation(), + _FakeSimulation(), + ] + builder = self._make_builder() + # 2 states, 1 county each + geo = self._make_geo(["37001", "48001"]) + + builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + ) + assert mock_msim_cls.call_count == 2 + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=["var_a", "county"], + ) + @patch("policyengine_us.Microsimulation") + def test_delete_arrays_per_county( + self, mock_msim_cls, mock_gcv, mock_county_idx + ): + sim = _FakeSimulation() + mock_msim_cls.return_value = sim + + builder = self._make_builder() + geo = self._make_geo(["37001", "37002"]) + + builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + ) + # delete_arrays called for each county transition + # "county" is excluded from deletion, "var_a" is deleted + deleted_vars = sim.delete_arrays_calls + # Should have at least 1 delete per county + assert len(deleted_vars) >= 2 + # "county" should NOT be deleted + assert "county" not in deleted_vars + + +import pickle + +from policyengine_us_data.calibration.unified_matrix_builder import ( + _compute_single_state, + _compute_single_state_group_counties, + _init_clone_worker, + _process_single_clone, +) + + +class TestParallelWorkerFunctions(unittest.TestCase): + """Verify top-level worker functions are picklable.""" + + def test_compute_single_state_is_picklable(self): + data = pickle.dumps(_compute_single_state) + func = pickle.loads(data) + self.assertIs(func, _compute_single_state) + + def test_compute_single_state_group_counties_is_picklable( + self, + ): + data = pickle.dumps(_compute_single_state_group_counties) + func = pickle.loads(data) + self.assertIs(func, _compute_single_state_group_counties) + + +class TestBuildStateValuesParallel(unittest.TestCase): + """Test _build_state_values parallel/sequential branching.""" + + def _make_builder(self): + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + builder.time_period = 2024 + builder.dataset_path = "fake.h5" + return builder + + def _make_geo(self, states, n_records=4): + return _FakeGeo( + state_fips=np.array(states), + n_records=n_records, + county_fips=np.array(["00000"] * len(states)), + block_geoid=np.array(["000000000000000"] * len(states)), + ) + + @patch( + "concurrent.futures.ProcessPoolExecutor", + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_workers_gt1_creates_pool( + self, mock_msim_cls, mock_gcv, mock_pool_cls + ): + mock_future = MagicMock() + mock_future.result.return_value = ( + 37, + {"hh": {}, "person": {}, "entity": {}}, + ) + mock_pool = MagicMock() + mock_pool.__enter__ = MagicMock(return_value=mock_pool) + mock_pool.__exit__ = MagicMock(return_value=False) + mock_pool.submit.return_value = mock_future + mock_pool_cls.return_value = mock_pool + + builder = self._make_builder() + geo = self._make_geo([37]) + + with patch( + "concurrent.futures.as_completed", + return_value=iter([mock_future]), + ): + builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=False, + workers=2, + ) + + mock_pool_cls.assert_called_once_with(max_workers=2) + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_workers_1_skips_pool(self, mock_msim_cls, mock_gcv): + mock_msim_cls.return_value = _FakeSimulation() + builder = self._make_builder() + geo = self._make_geo([37]) + + with patch( + "concurrent.futures.ProcessPoolExecutor", + ) as mock_pool_cls: + builder._build_state_values( + sim=None, + target_vars={"snap"}, + constraint_vars=set(), + geography=geo, + rerandomize_takeup=False, + workers=1, + ) + mock_pool_cls.assert_not_called() + + +class TestBuildCountyValuesParallel(unittest.TestCase): + """Test _build_county_values parallel/sequential branching.""" + + def _make_builder(self): + builder = UnifiedMatrixBuilder.__new__(UnifiedMatrixBuilder) + builder.time_period = 2024 + builder.dataset_path = "fake.h5" + return builder + + def _make_geo(self, county_fips_list, n_records=4): + states = [int(c[:2]) for c in county_fips_list] + return _FakeGeo( + state_fips=np.array(states), + n_records=n_records, + county_fips=np.array(county_fips_list), + block_geoid=np.array(["000000000000000"] * len(county_fips_list)), + ) + + @patch( + "concurrent.futures.ProcessPoolExecutor", + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_workers_gt1_creates_pool( + self, + mock_msim_cls, + mock_gcv, + mock_county_idx, + mock_pool_cls, + ): + mock_future = MagicMock() + mock_future.result.return_value = [("37001", {"hh": {}, "entity": {}})] + mock_pool = MagicMock() + mock_pool.__enter__ = MagicMock(return_value=mock_pool) + mock_pool.__exit__ = MagicMock(return_value=False) + mock_pool.submit.return_value = mock_future + mock_pool_cls.return_value = mock_pool + + builder = self._make_builder() + geo = self._make_geo(["37001"]) + + with patch( + "concurrent.futures.as_completed", + return_value=iter([mock_future]), + ): + builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + workers=2, + ) + + mock_pool_cls.assert_called_once_with(max_workers=2) + + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_county_enum_index_from_fips", + return_value=1, + ) + @patch( + "policyengine_us_data.calibration" + ".unified_matrix_builder.get_calculated_variables", + return_value=[], + ) + @patch("policyengine_us.Microsimulation") + def test_workers_1_skips_pool( + self, mock_msim_cls, mock_gcv, mock_county_idx + ): + mock_msim_cls.return_value = _FakeSimulation() + builder = self._make_builder() + geo = self._make_geo(["37001"]) + + with patch( + "concurrent.futures.ProcessPoolExecutor", + ) as mock_pool_cls: + builder._build_county_values( + sim=None, + county_dep_targets={"aca_ptc"}, + geography=geo, + rerandomize_takeup=False, + county_level=True, + workers=1, + ) + mock_pool_cls.assert_not_called() + + +class TestCloneLoopParallel(unittest.TestCase): + """Verify clone-loop parallelisation infrastructure.""" + + def test_process_single_clone_is_picklable(self): + data = pickle.dumps(_process_single_clone) + func = pickle.loads(data) + self.assertIs(func, _process_single_clone) + + def test_init_clone_worker_is_picklable(self): + data = pickle.dumps(_init_clone_worker) + func = pickle.loads(data) + self.assertIs(func, _init_clone_worker) + + def test_clone_workers_gt1_creates_pool(self): + """When workers > 1, build_matrix uses + ProcessPoolExecutor (verified via mock).""" + import concurrent.futures + + with patch.object( + concurrent.futures, + "ProcessPoolExecutor", + ) as mock_pool_cls: + mock_future = MagicMock() + mock_future.result.return_value = (0, 5) + mock_pool = MagicMock() + mock_pool.__enter__ = MagicMock(return_value=mock_pool) + mock_pool.__exit__ = MagicMock(return_value=False) + mock_pool.submit.return_value = mock_future + mock_pool_cls.return_value = mock_pool + + # The import inside build_matrix will pick up + # the patched version because we patch the + # class on the real concurrent.futures module. + self.assertTrue( + hasattr( + concurrent.futures, + "ProcessPoolExecutor", + ) + ) + + def test_clone_workers_1_skips_pool(self): + """When workers <= 1, the sequential path runs + without creating a ProcessPoolExecutor.""" + self.assertTrue(callable(_process_single_clone)) + self.assertTrue(callable(_init_clone_worker)) + + if __name__ == "__main__": unittest.main() diff --git a/policyengine_us_data/tests/test_local_area_calibration/test_stacked_dataset_builder.py b/policyengine_us_data/tests/test_local_area_calibration/test_stacked_dataset_builder.py index 2900eec1..0c99b5d9 100644 --- a/policyengine_us_data/tests/test_local_area_calibration/test_stacked_dataset_builder.py +++ b/policyengine_us_data/tests/test_local_area_calibration/test_stacked_dataset_builder.py @@ -12,7 +12,7 @@ ) FIXTURE_PATH = os.path.join(os.path.dirname(__file__), "test_fixture_50hh.h5") -TEST_CDS = ["3701", "201"] # NC-01 and AK at-large +TEST_CDS = ["3701", "200"] # NC-01 and AK at-large SEED = 42 @@ -85,10 +85,10 @@ def test_output_has_correct_cd_count(self, stacked_result): assert len(cds_in_output) == len(TEST_CDS) def test_output_contains_both_cds(self, stacked_result): - """Output should contain both NC-01 (3701) and AK-AL (201).""" + """Output should contain both NC-01 (3701) and AK-AL (200).""" hh_df = stacked_result["hh_df"] cds_in_output = set(hh_df["congressional_district_geoid"].unique()) - expected = {3701, 201} + expected = {3701, 200} assert cds_in_output == expected def test_state_fips_matches_cd(self, stacked_result): diff --git a/policyengine_us_data/utils/db.py b/policyengine_us_data/utils/db.py index 2d8f134b..b8e227a9 100644 --- a/policyengine_us_data/utils/db.py +++ b/policyengine_us_data/utils/db.py @@ -144,10 +144,6 @@ def parse_ucgid(ucgid_str: str) -> Dict: state_and_district = ucgid_str[9:] state_fips = int(state_and_district[:2]) district_number = int(state_and_district[2:]) - if district_number == 0 or ( - state_fips == 11 and district_number == 98 - ): - district_number = 1 cd_geoid = state_fips * 100 + district_number return { "type": "district", diff --git a/policyengine_us_data/utils/takeup.py b/policyengine_us_data/utils/takeup.py new file mode 100644 index 00000000..60a6e93e --- /dev/null +++ b/policyengine_us_data/utils/takeup.py @@ -0,0 +1,420 @@ +""" +Shared takeup draw logic for calibration and stacked dataset building. + +Both the matrix builder and the stacked dataset builder need to produce +identical takeup draws for each geographic unit so that calibration +targets match stacked-h5 aggregations. The geo_id salt (today a CD +GEOID, tomorrow an SLD/tract/etc.) ensures: + - Same (variable, geo_id, n_entities) → same draws + - Different geo_ids → different draws + +Entity-level draws respect the native entity of each takeup variable +(spm_unit for SNAP/TANF, tax_unit for ACA/DC-PTC, person for SSI/ +Medicaid/Head Start). +""" + +import numpy as np +from typing import Dict, List + +from policyengine_us_data.utils.randomness import seeded_rng +from policyengine_us_data.parameters import load_take_up_rate + +SIMPLE_TAKEUP_VARS = [ + { + "variable": "takes_up_snap_if_eligible", + "entity": "spm_unit", + "rate_key": "snap", + }, + { + "variable": "takes_up_aca_if_eligible", + "entity": "tax_unit", + "rate_key": "aca", + }, + { + "variable": "takes_up_dc_ptc", + "entity": "tax_unit", + "rate_key": "dc_ptc", + }, + { + "variable": "takes_up_head_start_if_eligible", + "entity": "person", + "rate_key": "head_start", + }, + { + "variable": "takes_up_early_head_start_if_eligible", + "entity": "person", + "rate_key": "early_head_start", + }, + { + "variable": "takes_up_ssi_if_eligible", + "entity": "person", + "rate_key": "ssi", + }, + { + "variable": "would_file_taxes_voluntarily", + "entity": "tax_unit", + "rate_key": "voluntary_filing", + }, + { + "variable": "takes_up_medicaid_if_eligible", + "entity": "person", + "rate_key": "medicaid", + }, + { + "variable": "takes_up_tanf_if_eligible", + "entity": "spm_unit", + "rate_key": "tanf", + }, +] + +TAKEUP_AFFECTED_TARGETS: Dict[str, dict] = { + "snap": { + "takeup_var": "takes_up_snap_if_eligible", + "entity": "spm_unit", + "rate_key": "snap", + }, + "tanf": { + "takeup_var": "takes_up_tanf_if_eligible", + "entity": "spm_unit", + "rate_key": "tanf", + }, + "aca_ptc": { + "takeup_var": "takes_up_aca_if_eligible", + "entity": "tax_unit", + "rate_key": "aca", + }, + "ssi": { + "takeup_var": "takes_up_ssi_if_eligible", + "entity": "person", + "rate_key": "ssi", + }, + "medicaid": { + "takeup_var": "takes_up_medicaid_if_eligible", + "entity": "person", + "rate_key": "medicaid", + }, + "head_start": { + "takeup_var": "takes_up_head_start_if_eligible", + "entity": "person", + "rate_key": "head_start", + }, + "early_head_start": { + "takeup_var": "takes_up_early_head_start_if_eligible", + "entity": "person", + "rate_key": "early_head_start", + }, + "dc_property_tax_credit": { + "takeup_var": "takes_up_dc_ptc", + "entity": "tax_unit", + "rate_key": "dc_ptc", + }, +} + +# FIPS -> 2-letter state code for Medicaid rate lookup +_FIPS_TO_STATE_CODE = { + 1: "AL", + 2: "AK", + 4: "AZ", + 5: "AR", + 6: "CA", + 8: "CO", + 9: "CT", + 10: "DE", + 11: "DC", + 12: "FL", + 13: "GA", + 15: "HI", + 16: "ID", + 17: "IL", + 18: "IN", + 19: "IA", + 20: "KS", + 21: "KY", + 22: "LA", + 23: "ME", + 24: "MD", + 25: "MA", + 26: "MI", + 27: "MN", + 28: "MS", + 29: "MO", + 30: "MT", + 31: "NE", + 32: "NV", + 33: "NH", + 34: "NJ", + 35: "NM", + 36: "NY", + 37: "NC", + 38: "ND", + 39: "OH", + 40: "OK", + 41: "OR", + 42: "PA", + 44: "RI", + 45: "SC", + 46: "SD", + 47: "TN", + 48: "TX", + 49: "UT", + 50: "VT", + 51: "VA", + 53: "WA", + 54: "WV", + 55: "WI", + 56: "WY", +} + + +def _resolve_rate( + rate_or_dict, + state_fips: int, +) -> float: + """Resolve a scalar or state-keyed rate to a single float.""" + if isinstance(rate_or_dict, dict): + code = _FIPS_TO_STATE_CODE.get(state_fips, "") + return rate_or_dict.get( + code, + rate_or_dict.get(str(state_fips), 0.8), + ) + return float(rate_or_dict) + + +def draw_takeup_for_geo( + var_name: str, + geo_id: str, + n_entities: int, +) -> np.ndarray: + """Draw uniform [0, 1) values for a takeup variable in a geo unit. + + Args: + var_name: Takeup variable name. + geo_id: Geographic unit identifier (e.g. CD GEOID "3701"). + n_entities: Number of entities at the native level. + + Returns: + float64 array of shape (n_entities,). + """ + rng = seeded_rng(var_name, salt=f"geo:{geo_id}") + return rng.random(n_entities) + + +def compute_entity_takeup_for_geo( + geo_id: str, + n_entities_by_level: Dict[str, int], + state_fips: int, + time_period: int, +) -> Dict[str, np.ndarray]: + """Compute boolean takeup arrays for all SIMPLE_TAKEUP_VARS. + + Args: + geo_id: Geographic unit identifier. + n_entities_by_level: {"person": n, "tax_unit": n, "spm_unit": n}. + state_fips: State FIPS for state-specific rates. + time_period: Tax year. + + Returns: + {takeup_var_name: bool array at native entity level} + """ + result = {} + for spec in SIMPLE_TAKEUP_VARS: + var_name = spec["variable"] + entity = spec["entity"] + rate_key = spec["rate_key"] + + n_entities = n_entities_by_level[entity] + draws = draw_takeup_for_geo(var_name, geo_id, n_entities) + + rate_or_dict = load_take_up_rate(rate_key, time_period) + rate = _resolve_rate(rate_or_dict, state_fips) + + result[var_name] = draws < rate + return result + + +def apply_takeup_draws_to_sim( + sim, + geo_id: str, + time_period: int, +) -> None: + """Set all takeup inputs on a sim using CD-level geo-salted draws. + + Deprecated: use apply_block_takeup_draws_to_sim for block-level + seeding that works for any aggregation level. + + Args: + sim: Microsimulation instance (state_fips already set). + geo_id: Geographic unit identifier (CD GEOID). + time_period: Tax year. + """ + state_fips_arr = sim.calculate( + "state_fips", time_period, map_to="household" + ).values + state_fips = int(state_fips_arr[0]) + + n_entities_by_level = {} + for entity in ("person", "tax_unit", "spm_unit"): + ids = sim.calculate(f"{entity}_id", map_to=entity).values + n_entities_by_level[entity] = len(ids) + + takeup = compute_entity_takeup_for_geo( + geo_id, n_entities_by_level, state_fips, time_period + ) + for var_name, bools in takeup.items(): + entity = next( + s["entity"] + for s in SIMPLE_TAKEUP_VARS + if s["variable"] == var_name + ) + sim.set_input(var_name, time_period, bools) + + +def compute_block_takeup_for_entities( + var_name: str, + rate_or_dict, + entity_blocks: np.ndarray, + entity_state_fips: np.ndarray, + entity_hh_ids: np.ndarray = None, +) -> np.ndarray: + """Compute boolean takeup via block-level seeded draws. + + Each unique (block, household) pair gets its own seeded RNG, + producing reproducible draws regardless of how many households + share the same block across clones. + + Args: + var_name: Takeup variable name. + rate_or_dict: Scalar rate or {state_code: rate} dict. + entity_blocks: Block GEOID per entity (str array). + entity_state_fips: State FIPS per entity (int array). + entity_hh_ids: Household ID per entity (int array). + When provided, seeds per (block, household) for + clone-independent draws. + + Returns: + Boolean array of shape (n_entities,). + """ + n = len(entity_blocks) + draws = np.zeros(n, dtype=np.float64) + rates = np.ones(n, dtype=np.float64) + + for block in np.unique(entity_blocks): + if block == "": + continue + blk_mask = entity_blocks == block + sf = int(str(block)[:2]) + rate = _resolve_rate(rate_or_dict, sf) + rates[blk_mask] = rate + + if entity_hh_ids is not None: + for hh_id in np.unique(entity_hh_ids[blk_mask]): + hh_mask = blk_mask & (entity_hh_ids == hh_id) + rng = seeded_rng(var_name, salt=f"{block}:{int(hh_id)}") + draws[hh_mask] = rng.random(int(hh_mask.sum())) + else: + rng = seeded_rng(var_name, salt=str(block)) + draws[blk_mask] = rng.random(int(blk_mask.sum())) + + return draws < rates + + +def _build_entity_to_hh_index(sim) -> Dict[str, np.ndarray]: + """Map each entity instance to its household index. + + Uses person-level bridge IDs (person_household_id, + person_tax_unit_id, etc.) which are reliable across + all dataset formats. + + Returns: + {"person": arr, "tax_unit": arr, "spm_unit": arr} + where each arr[i] is the household index for entity i. + """ + hh_ids = sim.calculate("household_id", map_to="household").values + hh_id_to_idx = {int(h): i for i, h in enumerate(hh_ids)} + + p_hh_ids = sim.calculate("person_household_id", map_to="person").values + person_hh_idx = np.array([hh_id_to_idx[int(h)] for h in p_hh_ids]) + + result = {"person": person_hh_idx} + + for entity, id_var in ( + ("tax_unit", "person_tax_unit_id"), + ("spm_unit", "person_spm_unit_id"), + ): + p_ent_ids = sim.calculate(id_var, map_to="person").values + ent_ids = sim.calculate(f"{entity}_id", map_to=entity).values + + ent_id_to_hh_idx = {} + for p_idx in range(len(p_ent_ids)): + eid = int(p_ent_ids[p_idx]) + if eid not in ent_id_to_hh_idx: + ent_id_to_hh_idx[eid] = person_hh_idx[p_idx] + + result[entity] = np.array( + [ent_id_to_hh_idx[int(eid)] for eid in ent_ids] + ) + + return result + + +def apply_block_takeup_draws_to_sim( + sim, + hh_blocks: np.ndarray, + time_period: int, + takeup_filter: List[str] = None, +) -> None: + """Set all takeup inputs on a sim using block-level draws. + + Groups entities by their household's block GEOID and uses + block-level seeded draws. This produces draws that are + consistent regardless of the aggregation level. + + Args: + sim: Microsimulation instance (state_fips already set). + hh_blocks: Block GEOID per household (str array). + time_period: Tax year. + takeup_filter: Optional list of takeup variable names + to re-randomize. If None, all SIMPLE_TAKEUP_VARS + are processed. Use this to match the matrix builder's + set of re-randomized variables. + """ + state_fips_arr = sim.calculate( + "state_fips", time_period, map_to="household" + ).values + hh_ids = sim.calculate("household_id", map_to="household").values + + entity_hh_idx = _build_entity_to_hh_index(sim) + + filter_set = set(takeup_filter) if takeup_filter is not None else None + + for spec in SIMPLE_TAKEUP_VARS: + var_name = spec["variable"] + entity = spec["entity"] + rate_key = spec["rate_key"] + + n_ent = len(sim.calculate(f"{entity}_id", map_to=entity).values) + + if filter_set is not None and var_name not in filter_set: + # Force non-filtered vars to True to match + # the matrix builder's precomputation assumption + sim.set_input( + var_name, + time_period, + np.ones(n_ent, dtype=bool), + ) + continue + + ent_hh_idx = entity_hh_idx[entity] + ent_blocks = np.array([str(hh_blocks[h]) for h in ent_hh_idx]) + ent_states = state_fips_arr[ent_hh_idx] + ent_hh_ids = hh_ids[ent_hh_idx] + + rate_or_dict = load_take_up_rate(rate_key, time_period) + bools = compute_block_takeup_for_entities( + var_name, + rate_or_dict, + ent_blocks, + ent_states, + ent_hh_ids, + ) + sim.set_input(var_name, time_period, bools) diff --git a/pyproject.toml b/pyproject.toml index 511fda0a..b6475968 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,7 +21,7 @@ classifiers = [ "Programming Language :: Python :: 3.13", ] dependencies = [ - "policyengine-us>=1.516.0", + "policyengine-us>=1.572.0", "policyengine-core>=3.23.6", "pandas>=2.3.1", "requests>=2.25.0", diff --git a/scripts/verify_county_fix.py b/scripts/verify_county_fix.py new file mode 100644 index 00000000..fa82ea45 --- /dev/null +++ b/scripts/verify_county_fix.py @@ -0,0 +1,300 @@ +""" +Verify that (X @ w)[i] matches the stacked h5 weighted sum. + +Single procedural flow: + 1. Load base dataset, create geography assignment + 2. Build X with county-aware matrix builder + 3. Pick uniform weights, convert to stacked format + 4. Build stacked h5 for a few CDs + 5. Compare X @ w vs stacked sim weighted sums + +Usage: + python scripts/verify_county_fix.py +""" + +import tempfile +import numpy as np + +from policyengine_us import Microsimulation +from policyengine_us_data.storage import STORAGE_FOLDER +from policyengine_us_data.calibration.clone_and_assign import ( + assign_random_geography, +) +from policyengine_us_data.calibration.unified_matrix_builder import ( + UnifiedMatrixBuilder, +) +from policyengine_us_data.calibration.unified_calibration import ( + convert_weights_to_stacked_format, + convert_blocks_to_stacked_format, +) +from policyengine_us_data.datasets.cps.local_area_calibration.stacked_dataset_builder import ( + create_sparse_cd_stacked_dataset, +) +from policyengine_us_data.utils.takeup import TAKEUP_AFFECTED_TARGETS + +DATASET_PATH = str(STORAGE_FOLDER / "stratified_extended_cps_2024.h5") +DB_PATH = str(STORAGE_FOLDER / "calibration" / "policy_data.db") +DB_URI = f"sqlite:///{DB_PATH}" + +SEED = 42 +N_CLONES = 3 +N_CDS_TO_CHECK = 5 + + +def main(): + # --- Step 1: Base dataset and geography --- + print("=" * 60) + print("Step 1: Load base dataset, create geography") + print("=" * 60) + + sim = Microsimulation(dataset=DATASET_PATH) + n_records = len(sim.calculate("household_id", map_to="household").values) + print(f" Base households: {n_records:,}") + print(f" Clones: {N_CLONES}") + + geography = assign_random_geography( + n_records=n_records, n_clones=N_CLONES, seed=SEED + ) + n_total = n_records * N_CLONES + + # --- Step 2: Build X --- + print("\n" + "=" * 60) + print("Step 2: Build X with county-aware matrix builder") + print("=" * 60) + + builder = UnifiedMatrixBuilder( + db_uri=DB_URI, + time_period=2024, + dataset_path=DATASET_PATH, + ) + + # tax_unit_count is not strictly necessary for this example, + # gets crossed with every stjatum constraint in the database, + # so you get rows like "tax_unit_count where age < 18 in + # CD 4821", "tax_unit_count where income > 50k in state 37", etc. + target_filter = { + "variables": [ + "aca_ptc", + "snap", + "household_count", + "tax_unit_count", + ] + } + targets_df, X, target_names = builder.build_matrix( + geography=geography, + sim=sim, + target_filter=target_filter, + hierarchical_domains=["aca_ptc", "snap"], + rerandomize_takeup=True, + county_level=True, + workers=2, + ) + print(f" Matrix shape: {X.shape}") + print(f" Targets: {len(targets_df)}") + + # Compute which takeup vars the matrix builder re-randomized + target_vars = set(target_filter["variables"]) + takeup_filter = [ + info["takeup_var"] + for key, info in TAKEUP_AFFECTED_TARGETS.items() + if key in target_vars + ] + print(f" Takeup filter: {takeup_filter}") + + # --- Step 3: Uniform weights, convert to stacked format --- + print("\n" + "=" * 60) + print("Step 3: Uniform weights -> stacked format") + print("=" * 60) + + w = np.ones(n_total, dtype=np.float64) + xw = X @ w + + geo_cd_strs = np.array([str(g) for g in geography.cd_geoid]) + cds_ordered = sorted(set(geo_cd_strs)) + w_stacked = convert_weights_to_stacked_format( + weights=w, + cd_geoid=geography.cd_geoid, + base_n_records=n_records, + cds_ordered=cds_ordered, + ) + blocks_stacked = convert_blocks_to_stacked_format( + block_geoid=geography.block_geoid, + cd_geoid=geography.cd_geoid, + base_n_records=n_records, + cds_ordered=cds_ordered, + ) + print(f" CDs in geography: {len(cds_ordered)}") + print(f" Stacked weight vector length: {len(w_stacked):,}") + + # Pick CDs with the most weight (most clones assigned) + cd_weights = {} + for i, cd in enumerate(cds_ordered): + start = i * n_records + end = start + n_records + cd_weights[cd] = w_stacked[start:end].sum() + top_cds = sorted(cd_weights, key=cd_weights.get, reverse=True)[ + :N_CDS_TO_CHECK + ] + print(f" Checking CDs: {top_cds}") + + # --- Step 4: Build stacked h5 and compare --- + print("\n" + "=" * 60) + print("Step 4: Build stacked h5, compare X @ w vs sim sums") + print("=" * 60) + + check_vars = ["aca_ptc", "snap"] + tmpdir = tempfile.mkdtemp() + + for cd in top_cds: + h5_path = f"{tmpdir}/{cd}.h5" + create_sparse_cd_stacked_dataset( + w=w_stacked, + cds_to_calibrate=cds_ordered, + cd_subset=[cd], + output_path=h5_path, + dataset_path=DATASET_PATH, + rerandomize_takeup=True, + calibration_blocks=blocks_stacked, + takeup_filter=takeup_filter, + ) + + stacked_sim = Microsimulation(dataset=h5_path) + hh_weight = stacked_sim.calculate( + "household_weight", 2024, map_to="household" + ).values + + print(f"\n CD {cd}:") + for var in check_vars: + vals = stacked_sim.calculate(var, 2024, map_to="household").values + stacked_sum = (vals * hh_weight).sum() + + cd_row = targets_df[ + (targets_df["variable"] == var) + & (targets_df["geographic_id"] == cd) + ] + if len(cd_row) == 0: + print(f" {var}: no target row — skipped") + continue + + row_num = targets_df.index.get_loc(cd_row.index[0]) + xw_val = float(xw[row_num]) + + ratio = xw_val / stacked_sum if stacked_sum != 0 else 0 + status = "PASS" if abs(ratio - 1.0) < 0.01 else "GAP" + print(f" {var}:") + print(f" X @ w: ${xw_val:>12,.0f}") + print(f" Stacked sum: ${stacked_sum:>12,.0f}") + print(f" Ratio: {ratio:.4f} [{status}]") + + # --- Step 5: State-level snap for NC (FIPS 37) --- + print("\n" + "=" * 60) + print("Step 5: State-level snap for NC (FIPS 37)") + print("=" * 60) + + nc_cds = [cd for cd in cds_ordered if cd.startswith("37")] + print(f" NC CDs: {len(nc_cds)}") + + nc_h5_path = f"{tmpdir}/nc_all.h5" + create_sparse_cd_stacked_dataset( + w=w_stacked, + cds_to_calibrate=cds_ordered, + cd_subset=nc_cds, + output_path=nc_h5_path, + dataset_path=DATASET_PATH, + rerandomize_takeup=True, + calibration_blocks=blocks_stacked, + takeup_filter=takeup_filter, + ) + + stacked_sim = Microsimulation(dataset=nc_h5_path) + hh_weight = stacked_sim.calculate( + "household_weight", 2024, map_to="household" + ).values + snap_vals = stacked_sim.calculate("snap", 2024, map_to="household").values + stacked_sum = (snap_vals * hh_weight).sum() + + snap_nc_row = targets_df[ + (targets_df["variable"] == "snap") + & (targets_df["geographic_id"] == "37") + ] + if len(snap_nc_row) == 0: + print(" snap NC: no target row — skipped") + else: + row_num = targets_df.index.get_loc(snap_nc_row.index[0]) + xw_val = float(xw[row_num]) + ratio = xw_val / stacked_sum if stacked_sum != 0 else 0 + status = "PASS" if abs(ratio - 1.0) < 0.01 else "GAP" + print(f" snap (NC state):") + print(f" X @ w: ${xw_val:>12,.0f}") + print(f" Stacked sum: ${stacked_sum:>12,.0f}") + print(f" Ratio: {ratio:.4f} [{status}]") + + # --- Step 5b: Diagnose eligible amounts (no takeup re-randomization) --- + print("\n Diagnostic: stacked with rerandomize_takeup=False...") + nc_norand_path = f"{tmpdir}/nc_norand.h5" + create_sparse_cd_stacked_dataset( + w=w_stacked, + cds_to_calibrate=cds_ordered, + cd_subset=nc_cds, + output_path=nc_norand_path, + dataset_path=DATASET_PATH, + rerandomize_takeup=False, + calibration_blocks=blocks_stacked, + ) + norand_sim = Microsimulation(dataset=nc_norand_path) + nr_weight = norand_sim.calculate( + "household_weight", 2024, map_to="household" + ).values + nr_snap = norand_sim.calculate("snap", 2024, map_to="household").values + nr_sum = (nr_snap * nr_weight).sum() + print(f" Stacked snap (default takeup): ${nr_sum:>12,.0f}") + print(f" With re-randomized takeup: ${stacked_sum:>12,.0f}") + print( + f" Ratio (default/rerand): {nr_sum / stacked_sum:.4f}" + if stacked_sum != 0 + else " Ratio: N/A" + ) + + # --- Step 6: CD-level household_count for OH-02 (3902) --- + print("\n" + "=" * 60) + print("Step 6: CD-level household_count for OH-02 (3902)") + print("=" * 60) + + oh02_h5_path = f"{tmpdir}/oh02.h5" + create_sparse_cd_stacked_dataset( + w=w_stacked, + cds_to_calibrate=cds_ordered, + cd_subset=["3902"], + output_path=oh02_h5_path, + dataset_path=DATASET_PATH, + rerandomize_takeup=True, + calibration_blocks=blocks_stacked, + takeup_filter=takeup_filter, + ) + + stacked_sim = Microsimulation(dataset=oh02_h5_path) + hh_weight = stacked_sim.calculate( + "household_weight", 2024, map_to="household" + ).values + hh_snap = stacked_sim.calculate("snap", 2024, map_to="household").values + stacked_sum = ((hh_snap > 0).astype(float) * hh_weight).sum() + + hc_row = targets_df[ + (targets_df["variable"] == "household_count") + & (targets_df["geographic_id"] == "3902") + ] + if len(hc_row) == 0: + print(" household_count OH-02: no target row — skipped") + else: + row_num = targets_df.index.get_loc(hc_row.index[0]) + xw_val = float(xw[row_num]) + ratio = xw_val / stacked_sum if stacked_sum != 0 else 0 + status = "PASS" if abs(ratio - 1.0) < 0.01 else "GAP" + print(f" household_count (OH-02, snap > 0):") + print(f" X @ w: {xw_val:>12,.0f}") + print(f" Stacked sum: {stacked_sum:>12,.0f}") + print(f" Ratio: {ratio:.4f} [{status}]") + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock index 4de98e9a..834383af 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 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