Update publication candidate

Author: MaxGhenis

changelog.d/1125.changed.md …512098270-a1/changelog.d/1125.changed.mdchangelog.d/1125.changed.md renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1125.changed.md changelog.d/1125.changed.md renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1125.changed.md

@@ -0,0 +1,7 @@
+{
+  "base_release_version": "1.115.5",
+  "candidate_scope": "1.115.5-patch",
+  "release_bump": "patch",
+  "run_id": "usdata-gha26512098270-a1",
+  "would_release_as_at_build_time": "1.115.6"
+}

changelog.d/1131.fixed …gha26512098270-a1/changelog.d/1131.fixedchangelog.d/1131.fixed renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1131.fixed changelog.d/1131.fixed renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1131.fixed

@@ -2,6 +2,6 @@
   "base_release_version": "1.115.5",
   "candidate_scope": "1.115.5-patch",
   "release_bump": "patch",
-  "run_id": "usdata-gha26421187461-a1",
+  "run_id": "usdata-gha26512098270-a1",
   "would_release_as_at_build_time": "1.115.6"
 }

changelog.d/1141.changed.md …512098270-a1/changelog.d/1141.changed.mdchangelog.d/1141.changed.md renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1141.changed.md changelog.d/1141.changed.md renamed to .github/publication_candidates/usdata-gha26512098270-a1/changelog.d/1141.changed.md

@@ -3,7 +3,7 @@
     "docstring": "Set 2025 ACA take-up to match APTC enrollment targets.",
     "id": "aca_2025_override",
     "kind": "function",
-    "line": 404,
+    "line": 420,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.enhanced_cps.create_aca_2025_takeup_override"
@@ -34,7 +34,7 @@
     "docstring": "Impute rent and real_estate_taxes from ACS with state.\n\nArgs:\n    data: CPS data dict.\n    state_fips: State FIPS per household.\n    time_period: Tax year.\n    dataset_path: Path to CPS h5 for Microsimulation.\n\nReturns:\n    Updated data dict.",
     "id": "acs_qrf",
     "kind": "function",
-    "line": 524,
+    "line": 525,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute._impute_acs"
@@ -61,7 +61,7 @@
     "docstring": "\"Add auto loan balance, interest and net_worth variable.",
     "id": "add_auto_loan",
     "kind": "function",
-    "line": 3080,
+    "line": 3063,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_auto_loan_interest_and_net_worth"
@@ -88,7 +88,7 @@
     "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.",
     "id": "add_household_variables",
     "kind": "function",
-    "line": 1673,
+    "line": 1656,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_household_variables"
@@ -142,7 +142,7 @@
     "docstring": "Impute ORG-derived labor-market inputs and derive overtime premium.",
     "id": "add_org_inputs",
     "kind": "function",
-    "line": 2980,
+    "line": 2963,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_org_labor_market_inputs"
@@ -223,7 +223,7 @@
     "docstring": "",
     "id": "add_previous_year_income",
     "kind": "function",
-    "line": 1715,
+    "line": 1698,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_previous_year_income"
@@ -277,7 +277,7 @@
     "docstring": "",
     "id": "add_spm_variables",
     "kind": "function",
-    "line": 1634,
+    "line": 1617,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_spm_variables"
@@ -304,7 +304,7 @@
     "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",
     "id": "add_ssn_card_type",
     "kind": "function",
-    "line": 1821,
+    "line": 1804,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_ssn_card_type"
@@ -358,7 +358,7 @@
     "docstring": "",
     "id": "add_tips",
     "kind": "function",
-    "line": 2720,
+    "line": 2703,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_tips"
@@ -815,7 +815,7 @@
     "docstring": "Replace clone-half person-level feature variables with donor matches.",
     "id": "clone_features",
     "kind": "function",
-    "line": 607,
+    "line": 603,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._splice_clone_feature_predictions"
@@ -878,7 +878,7 @@
     "docstring": "Assert that final exported variables are leaf inputs.",
     "id": "computed_export_contract",
     "kind": "function",
-    "line": 1802,
+    "line": 1782,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._assert_no_computed_variables_exported"
@@ -972,7 +972,7 @@
     "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).",
     "id": "cps_only",
     "kind": "function",
-    "line": 646,
+    "line": 642,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._impute_cps_only_variables"
@@ -1325,7 +1325,7 @@
     "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.",
     "id": "housing_assistance_microsim_validation",
     "kind": "function",
-    "line": 1572,
+    "line": 1552,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation"
@@ -3243,7 +3243,7 @@
     "docstring": "Run QRF imputation for PUF variables.\n\nStratified-subsamples PUF records (top 0.5% by AGI kept,\nrest randomly sampled to ~20K total), trains QRF, and\npredicts on CPS data.\n\nArgs:\n    data: CPS data dict.\n    time_period: Tax year.\n    puf_dataset: PUF dataset class or path.\n    dataset_path: Path to CPS h5 for computing\n        demographic predictors via Microsimulation.\n\nReturns:\n    Tuple of (y_full_imputations, y_override_imputations)\n    as dicts of {variable: np.ndarray}.",
     "id": "puf_qrf_pass",
     "kind": "function",
-    "line": 914,
+    "line": 898,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.puf_impute._run_qrf_imputation"
@@ -3270,7 +3270,7 @@
     "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.",
     "id": "qrf_pass2",
     "kind": "function",
-    "line": 1037,
+    "line": 1017,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._splice_cps_only_predictions"
@@ -3562,7 +3562,7 @@
     "docstring": "",
     "id": "reweight",
     "kind": "function",
-    "line": 487,
+    "line": 503,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.enhanced_cps.reweight"
@@ -3697,7 +3697,7 @@
     "docstring": "Run structural integrity checks on an H5 file.\n\nArgs:\n    h5_path: Path to the H5 dataset file.\n    period: Tax year (used for variable keys).\n\nReturns:\n    List of {check, status, detail} dicts.",
     "id": "sanity_checks",
     "kind": "function",
-    "line": 331,
+    "line": 329,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.sanity_checks.run_sanity_checks"
@@ -3724,7 +3724,7 @@
     "docstring": "Impute net_worth and auto_loan from SCF.\n\nArgs:\n    data: CPS data dict.\n    state_fips: State FIPS per household.\n    time_period: Tax year.\n    dataset_path: Path to CPS h5 for Microsimulation.\n\nReturns:\n    Updated data dict.",
     "id": "scf_qrf",
     "kind": "function",
-    "line": 1108,
+    "line": 1113,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute._impute_scf"
@@ -3778,7 +3778,7 @@
     "docstring": "Impute tip_income, liquid assets, and vehicle signals from SIPP.\n\nArgs:\n    data: CPS data dict.\n    state_fips: State FIPS per household.\n    time_period: Tax year.\n    dataset_path: Path to CPS h5 for Microsimulation.\n\nReturns:\n    Updated data dict.",
     "id": "sipp_qrf",
     "kind": "function",
-    "line": 649,
+    "line": 650,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute._impute_sipp"
@@ -3805,7 +3805,7 @@
     "docstring": "Re-impute ACS/SIPP/ORG/SCF variables from donor surveys.\n\nOverwrites existing imputed values in data. ACS uses\nstate_fips as a QRF predictor; ORG uses state plus labor-market\npredictors; SIPP and SCF use only demographic and financial\npredictors (no state data).\n\nArgs:\n    data: CPS dataset dict {variable: {time_period: array}}.\n    state_fips: State FIPS per household.\n    time_period: Tax year.\n    dataset_path: Path to CPS h5 for Microsimulation.\n    skip_acs: Skip ACS imputation.\n    skip_sipp: Skip SIPP imputation.\n    skip_org: Skip ORG imputation.\n    skip_scf: Skip SCF imputation.\n\nReturns:\n    Updated data dict with re-imputed variables.",
     "id": "source_impute",
     "kind": "function",
-    "line": 219,
+    "line": 220,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute.impute_source_variables"