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A dataset reform simulation's baseline loses inputs the reform neutralizes

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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
python
領域
backend

調査の方向性

Run the reproduction script to confirm the reform baseline returns [0.0, 0.0] instead of [10.0, 20.0]. Read Simulation.__init__ and _bind_to_tax_benefit_system in simulations/simulation.py around lines 405-413, then Holder.set_input in holders/holder.py:460 to see where neutralized inputs are dropped. Compare with the situation-path fix in #613; the dataset path still needs to load the dataset under the baseline system. Done means the reform simulation returns defaults while reformed.baseline returns the dataset's original values.

索引モデルが issue の本文から書いたものです。

説明

Problem

When a simulation built from a dataset applies a reform that neutralizes an input variable, its baseline loses that input as well. The baseline then reads the variable's default, so reform-vs-baseline comparisons describe the wrong baseline.

Simulation.__init__ builds the reform simulation from the dataset under the reform's system. Holder.set_input ignores a value for a neutralized variable (holders/holder.py:460). The baseline is then get_branch("baseline"), rebound to the baseline system by _bind_to_tax_benefit_system (simulations/simulation.py:405-413 in 3.32.27). So the branch inherits holders that never received the column. The rebinding makes the variable un-neutralized again, but there is no data left to read.

#613 fixes the same gap for situations: it builds the baseline from the situation under the baseline system. It keeps the old binding when there is no situation (a dataset, or populations given), so the dataset path still has the bug.

Reproduction (core 3.32.27; master 037960cab has the same set_input and baseline code)

import warnings

import numpy as np
from policyengine_core.entities import build_entity
from policyengine_core.model_api import Variable, YEAR
from policyengine_core.simulations import Simulation
from policyengine_core.taxbenefitsystems import TaxBenefitSystem

Person = build_entity("person", "persons", "Person", is_person=True)


class person_id(Variable):
    value_type = int
    entity = Person
    definition_period = YEAR
    label = "Person ID"


class hours(Variable):
    value_type = float
    entity = Person
    definition_period = YEAR
    label = "Hours, an input"


class System(TaxBenefitSystem):
    def __init__(self, reform=None):
        super().__init__([Person])
        self.add_variables(person_id, hours)
        if reform is not None:
            self.apply_reform_set(reform)


class Sim(Simulation):
    default_tax_benefit_system = System
    default_tax_benefit_system_instance = System()


class Data:
    name = "repro"
    data_format = "arrays"
    time_period = "2024"
    file_path = None

    def load(self):
        return {"person_id": np.array([1, 2]), "hours": np.array([10.0, 20.0])}


class NeutralizeHours:
    def apply(self, system):
        system.neutralize_variable("hours")


with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    reformed = Sim(dataset=Data(), reform=NeutralizeHours())
print("plain simulation:   ", Sim(dataset=Data()).calculate("hours", 2024).tolist())
print("reform:             ", reformed.calculate("hours", 2024).tolist())
print("reform's baseline:  ", reformed.baseline.calculate("hours", 2024).tolist())

Output:

plain simulation:    [10.0, 20.0]
reform:              [0.0, 0.0]
reform's baseline:   [0.0, 0.0]

The baseline should read [10.0, 20.0], as the plain simulation does.

Possible fix

Build a dataset reform simulation's baseline branch by loading the dataset under the baseline system, as #613 does for situations. Alternatively, keep the dataset's values for neutralized variables, without exposing them to the reform, so the baseline branch can read them.

Found while reviewing PolicyEngine/policyengine-us#10447. A reform that neutralizes unemployment_compensation_start_month makes both the reform and its baseline treat the column as unsupplied.

🤖 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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