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Update publication candidate
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changelog.d/populate-spm-energy-subsidy.changed.md renamed to .github/publication_candidates/usdata-gha25970892240-a1/changelog.d/populate-spm-energy-subsidy.changed.md

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‎.github/publication_candidates/usdata-gha25970892240-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-gha25970892240-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-gha25968212826-a1",
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"run_id": "usdata-gha25970892240-a1",
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"would_release_as_at_build_time": "1.115.3"
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}

‎docs/generated/pipeline_api.json‎

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"docstring": "Set 2025 ACA take-up to match APTC enrollment targets.",
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"id": "aca_2025_override",
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"kind": "function",
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"line": 421,
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"line": 403,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.enhanced_cps.create_aca_2025_takeup_override"
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"docstring": "\"Add auto loan balance, interest and net_worth variable.",
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"id": "add_auto_loan",
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"kind": "function",
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"line": 2792,
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"line": 2798,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_auto_loan_interest_and_net_worth"
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"docstring": "Populate household-level geography variables used by PolicyEngine US.\n\nArgs:\n cps: Output CPS H5 group receiving derived household variables.\n household: Raw CPS household table.",
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"id": "add_household_variables",
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"kind": "function",
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"line": 1454,
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"line": 1460,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_household_variables"
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"docstring": "Add basic ID and weight variables.\n\nArgs:\n cps (h5py.File): The CPS dataset file.\n person (DataFrame): The person table of the ASEC.\n tax_unit (DataFrame): The tax unit table created from the person table\n of the ASEC.\n family (DataFrame): The family table of the ASEC.\n spm_unit (DataFrame): The SPM unit table created from the person table\n of the ASEC.\n household (DataFrame): The household table of the ASEC.",
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"id": "add_id_variables",
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"kind": "function",
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"line": 933,
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"line": 931,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_id_variables"
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"docstring": "Impute ORG-derived wage and union inputs onto CPS persons.",
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"id": "add_org_inputs",
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"kind": "function",
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"line": 2676,
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"line": 2682,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_org_labor_market_inputs"
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"docstring": "Add income variables.\n\nArgs:\n cps (h5py.File): The CPS dataset file.\n person (DataFrame): The CPS person table.\n year (int): The CPS year",
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"id": "add_personal_income_variables",
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"kind": "function",
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"line": 1137,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_personal_income_variables"
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"docstring": "Add personal demographic variables.\n\nArgs:\n cps (h5py.File): The CPS dataset file.\n person (DataFrame): The CPS person table.",
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"id": "add_personal_variables",
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"kind": "function",
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"line": 995,
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"line": 993,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_personal_variables"
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"docstring": "",
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"id": "add_previous_year_income",
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"kind": "function",
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"line": 1496,
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"line": 1502,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_previous_year_income"
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"docstring": "",
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"id": "add_spm_variables",
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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.cps.add_spm_variables"
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"docstring": "Assign SSN card type using PRCITSHP, employment status, and ASEC-UA conditions.\nCodes:\n- 0: \"NONE\" - Likely undocumented immigrants\n- 1: \"CITIZEN\" - US citizens (born or naturalized)\n- 2: \"NON_CITIZEN_VALID_EAD\" - Non-citizens with work/study authorization\n- 3: \"OTHER_NON_CITIZEN\" - Non-citizens with indicators of legal status",
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"id": "add_ssn_card_type",
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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.cps.add_ssn_card_type"
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"docstring": "",
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"id": "add_takeup",
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"kind": "function",
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"line": 474,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_takeup"
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"docstring": "",
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"id": "add_tips",
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"kind": "function",
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"line": 2507,
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"metadata": {
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"api_refs": [
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"policyengine_us_data.datasets.cps.cps.add_tips"
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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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"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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"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": 443,
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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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"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"
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"docstring": "",
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"id": "reweight",
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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.enhanced_cps.reweight"

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