Update publication candidate

Author: MaxGhenis

…-disability-status-imputation.changed.md …-disability-status-imputation.changed.mdchangelog.d/ssi-disability-status-imputation.changed.md renamed to .github/publication_candidates/usdata-gha26236774434-a1/changelog.d/ssi-disability-status-imputation.changed.md changelog.d/ssi-disability-status-imputation.changed.md renamed to .github/publication_candidates/usdata-gha26236774434-a1/changelog.d/ssi-disability-status-imputation.changed.md

@@ -0,0 +1,7 @@
+{
+  "base_release_version": "1.115.5",
+  "candidate_scope": "1.115.5-patch",
+  "release_bump": "patch",
+  "run_id": "usdata-gha26236774434-a1",
+  "would_release_as_at_build_time": "1.115.6"
+}

.github/publication_candidates/usdata-gha26236774434-a1/publication_scope.json

@@ -2,6 +2,6 @@
   "base_release_version": "1.115.5",
   "candidate_scope": "1.115.5-patch",
   "release_bump": "patch",
-  "run_id": "usdata-gha26232501461-a1",
+  "run_id": "usdata-gha26236774434-a1",
   "would_release_as_at_build_time": "1.115.6"
 }

.github/publication_scope.json

@@ -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": 485,
+    "line": 490,
     "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": 2859,
+    "line": 2894,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_auto_loan_interest_and_net_worth"
@@ -142,7 +142,7 @@
     "docstring": "Impute ORG-derived wage and union inputs onto CPS persons.",
     "id": "add_org_inputs",
     "kind": "function",
-    "line": 2743,
+    "line": 2778,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.cps.add_org_labor_market_inputs"
@@ -810,7 +810,7 @@
     "docstring": "Replace clone-half person-level feature variables with donor matches.",
     "id": "clone_features",
     "kind": "function",
-    "line": 411,
+    "line": 412,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._splice_clone_feature_predictions"
@@ -873,7 +873,7 @@
     "docstring": "Assert that final exported variables are leaf inputs.",
     "id": "computed_export_contract",
     "kind": "function",
-    "line": 1584,
+    "line": 1585,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._assert_no_computed_variables_exported"
@@ -967,7 +967,7 @@
     "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": 450,
+    "line": 451,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._impute_cps_only_variables"
@@ -1252,7 +1252,7 @@
     "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": 1354,
+    "line": 1355,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps.ExtendedCPS._validate_housing_assistance_microsimulation"
@@ -3075,7 +3075,7 @@
     "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": 824,
+    "line": 825,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.datasets.cps.extended_cps._splice_cps_only_predictions"
@@ -3461,7 +3461,7 @@
     "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": 922,
+    "line": 1004,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute._impute_scf"
@@ -3515,7 +3515,7 @@
     "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": 586,
+    "line": 591,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute._impute_sipp"
@@ -3542,7 +3542,7 @@
     "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": 180,
+    "line": 185,
     "metadata": {
       "api_refs": [
         "policyengine_us_data.calibration.source_impute.impute_source_variables"