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SimulationBuilder.build_from_dict should clear dataset provenance when rebuilding from a situation

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評価

難易度
4/5
見積もり時間
3〜5日
初心者へのやさしさ
45/100
issue の種類
バグ
明瞭さ
明確に書かれている
活発さ
活発
技術スタック
numpy, python
領域
backend, data

調査の方向性

Start with SimulationBuilder.build_from_dict, then trace build_from_entities and build_from_variables in simulation_builder.py alongside provenance initialization in simulation.py. Check how clones and branches copy source state. Done means both rebuild paths clear dataset provenance, record the active situation, and preserve correct provenance in derived clones and branches.

索引モデルが issue の本文から書いたものです。

説明

Found while adopting PolicyEngine/policyengine-uk#1831 (UK hub, 2026-10-20).

PolicyEngine Core version: 3.33.2. Reproduced with synthetic MOCK data and the UK tax-benefit system, without private datasets or downloads.

SimulationBuilder.build_from_dict(system, situation, simulation) supports overwriting an existing simulation. When that simulation was built over a dataset, the method replaces its population structure and inputs but leaves is_over_dataset=True and retains dataset. It also leaves situation_input=None. Its clones and newly created branches inherit this stale provenance.

Reproducer

import pandas as pd

from policyengine_core.simulations import Simulation
from policyengine_core.simulations.simulation_builder import SimulationBuilder
from policyengine_uk.system import system

year = 2026
frame = pd.DataFrame(
    {
        f"person_id__{year}": [1],
        f"person_benunit_id__{year}": [1],
        f"person_household_id__{year}": [1],
        f"benunit_id__{year}": [1],
        f"household_id__{year}": [1],
        f"age__{year}": [70],
    }
)
situation = {
    "people": {"adult": {"age": {year: 70}}},
    "benunits": {"family": {"members": ["adult"]}},
    "households": {"home": {"members": ["adult"]}},
}
simulation = Simulation(tax_benefit_system=system, dataset=frame)
original_dataset = simulation.dataset
builder = SimulationBuilder()
builder.default_period = year
rebuilt = builder.build_from_dict(system, situation, simulation)

assert rebuilt is simulation
print(rebuilt.persons.ids)                   # ['adult']
print(rebuilt.is_over_dataset)               # True (expected False)
print(rebuilt.dataset is original_dataset)    # True (expected cleared)
print(rebuilt.situation_input)               # None (no new situation marker)
print(rebuilt.clone().is_over_dataset)        # True (expected False)
print(rebuilt.get_branch("rebuilt").is_over_dataset)  # True (expected False)

Expected behavior

Rebuilding from a situation should reset is_over_dataset, clear the previous dataset, and record the situation as the active source. The behavior should cover both build_from_entities and build_from_variables, including clones and branches derived after the rebuild. A canonical source marker or population-rebuild hook would also let subclasses keep their own provenance in sync.

Source finding and downstream effect

In Core 3.33.2, Simulation.__init__ sets is_over_dataset (simulation.py:801) and situation_input (simulation.py:897). SimulationBuilder.build_from_dict (simulation_builder.py:66–96) only dispatches the build and refreshes input_variables; build_from_entities (98–192) updates population counts, IDs, memberships and holder inputs without recording a source change. The builder's population bookkeeping is not retained as an independent source marker on the simulation. Population IDs or input variable names are not reliable provenance: either source can use the same IDs and variables, and variable-only situations also use NumPy ID arrays.

The PE-UK provenance probe classifies 20 of 24 routes correctly. Its four failures are a data-built core simulation overwritten by this builder, its clone, its new branch, and a data-built UK simulation overwritten directly by the core builder. PE-UK's supported Simulation.build_from_situation rebuild path resets its own source marker correctly. No supported production caller found by the UK hub uses these four direct core-builder overwrite routes.

This matters for model formulas that distinguish survey runs from explicit household situations. PE-UK currently documents the direct overwrite limitation instead of guessing from IDs, weights or population size.

🤖 Generated with Claude Code

主要言語
Python
スター
22
フォーク
30
平均マージ
4日 16時間
マージ済み PR（30日）
35

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