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Update publication candidate
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changelog.d/995.changed.md renamed to .github/publication_candidates/usdata-gha26002017534-a1/changelog.d/995.changed.md

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‎.github/publication_candidates/usdata-gha26002017534-a1/publication_scope.json‎

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{
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"base_release_version": "1.115.2",
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"candidate_scope": "1.115.2-patch",
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"release_bump": "patch",
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"run_id": "usdata-gha26002017534-a1",
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"would_release_as_at_build_time": "1.115.3"
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}

‎.github/publication_scope.json‎

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"base_release_version": "1.115.2",
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"candidate_scope": "1.115.2-patch",
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"release_bump": "patch",
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"run_id": "usdata-gha25997959504-a1",
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"run_id": "usdata-gha26002017534-a1",
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"would_release_as_at_build_time": "1.115.3"
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}

‎docs/engineering/pipeline-map.md‎

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Create a stratified sample of CPS data preserving high-income households
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### `policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation`
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```python
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def _validate_housing_assistance_microsimulation(cls, data, time_period, microsimulation_cls = None)
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```
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Check formula-reconstructed housing assistance before export.
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### `policyengine_us_data.calibration.publish_local_area.load_calibration_geography`
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```python

‎docs/generated/pipeline_api.json‎

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"docstring": "Replace clone-half person-level feature variables with donor matches.",
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"id": "clone_features",
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"kind": "function",
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"line": 404,
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"line": 405,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps._splice_clone_feature_predictions"
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"docstring": "Assert that final exported variables are leaf inputs.",
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"id": "computed_export_contract",
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"kind": "function",
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"line": 1267,
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"line": 1449,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._assert_no_computed_variables_exported"
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"docstring": "Second-stage QRF: train on CPS, predict for PUF clones.\n\nFor the PUF clone half of the extended CPS we need plausible values\nof CPS-only variables (retirement distributions, transfers, hours,\nSPM components, etc.) that are consistent with the clone's\nPUF-imputed income -- not just naively copied from the CPS donor.\n\nWe train a QRF on CPS person-level data where:\n * predictors = demographics + key income variables\n * outputs = CPS-only variables listed in\n ``CPS_ONLY_IMPUTED_VARIABLES``\n\nFor PUF clone prediction we use the PUF-imputed income values\nfrom the second half of ``data`` (the clone half, which already\nhas PUF-imputed income from stage 1).\n\nUses ``fit_predict()`` with ``max_train_samples`` instead of\nmanual sampling + separate fit/predict.\n\nArgs:\n data: Extended dataset dict after ``puf_clone_dataset()`` --\n already doubled, with PUF-imputed income in the second half.\n time_period: Tax year.\n dataset_path: Path to the CPS h5 file for Microsimulation.\n\nReturns:\n DataFrame with one column per CPS-only variable, containing\n predicted values for the PUF clone half (person-level).",
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"id": "cps_only",
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"kind": "function",
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"line": 444,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps._impute_cps_only_variables"
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"signature": "def derive_geography_from_blocks(block_geoids: np.ndarray) -> Dict[str, np.ndarray]",
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"source_file": "policyengine_us_data/calibration/block_assignment.py"
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},
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"housing_assistance_microsim_validation": {
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"docstring": "Check formula-reconstructed housing assistance before export.\n\nThe final H5 must not export formula outputs such as ``housing_assistance``.\nThis guard verifies that the remaining leaf inputs still make those\nformulas produce nonzero values before the export contract strips or\nrejects computed variables.",
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"id": "housing_assistance_microsim_validation",
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"kind": "function",
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"line": 1321,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation"
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],
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"artifacts_in": [
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"extended_cps_stage2"
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],
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"artifacts_out": [
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"housing_validated_extended_cps"
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],
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"description": "Runs a temporary microsimulation before final export to ensure housing-assistance leaf inputs reconstruct nonzero modeled housing assistance and capped SPM housing subsidy.",
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"id": "housing_assistance_microsim_validation",
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"label": "Validate Housing Assistance Microsimulation",
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"node_type": "process",
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"pathways": [
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"data_build"
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],
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"pydoc": true,
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"source_file": "policyengine_us_data/datasets/cps/extended_cps.py",
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"stability": "moving",
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"status": "transitional"
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},
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"object_path": "policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation",
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"signature": "def _validate_housing_assistance_microsimulation(cls, data, time_period, microsimulation_cls = None)",
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"source_file": "policyengine_us_data/datasets/cps/extended_cps.py"
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},
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"impute_puf_demographics": {
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"docstring": "",
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"id": "impute_puf_demographics",
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"docstring": "Replace PUF clone half of CPS-only variables with QRF predictions.\n\nAfter ``puf_clone_dataset()`` the CPS-only variables in the second\nhalf are naive copies of the CPS donor values. This function\nreplaces them with the second-stage QRF predictions that are\nconsistent with the clone's PUF-imputed income.\n\nArgs:\n data: Extended dataset dict (already doubled).\n predictions: DataFrame from ``_impute_cps_only_variables()``.\n time_period: Tax year.\n dataset_path: Path to CPS h5 file for entity mapping.\n\nReturns:\n Modified data dict with CPS-only variables spliced in.",
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"id": "qrf_pass2",
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"kind": "function",
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps._splice_cps_only_predictions"

‎docs/generated/pipeline_map.json‎

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"uv run pytest tests/unit/calibration/test_create_stratified_cps.py"
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]
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},
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{
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"api_refs": [
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"policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation"
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],
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"artifacts_in": [
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"extended_cps_stage2"
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],
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"artifacts_out": [
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"housing_validated_extended_cps"
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],
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"description": "Runs a temporary microsimulation before final export to ensure housing-assistance leaf inputs reconstruct nonzero modeled housing assistance and capped SPM housing subsidy.",
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"id": "housing_assistance_microsim_validation",
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"label": "Validate Housing Assistance Microsimulation",
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"node_type": "process",
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"pathways": [
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"data_build"
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],
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"pydoc": true,
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"source_file": "policyengine_us_data/datasets/cps/extended_cps.py",
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"stability": "moving",
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"status": "transitional"
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},
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{
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"api_refs": [
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"policyengine_us_data.build_outputs.geography_loader.CalibrationGeographyLoader",
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}
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],
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"metadata": {
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"api_node_count": 63,
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"api_node_count": 64,
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"canonical_stage_count": 5,
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"decorated_object_count": 112,
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"decorated_object_count": 113,
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"mapped_decorated_node_count": 49,
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"stage_count": 17,
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"substage_count": 17

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