# modelx: pseudo-python
# This file is part of a modelx model.
# It can be imported as a Python module, but functions defined herein
# are model formulas and may not be executable as standard Python.
"""Input data shared by every by-policy projection.
The eight input CSVs are read here, **once per model**, and referenced from
:mod:`~.Dep_FR_S.Projection` as ``data``. :mod:`~.Dep_FR_S.Projection` is parameterized
by ``point_id``, so each ``Projection[N]`` is a separate ItemSpace with its own cells
cache; if the readers lived there, every model point would re-read every file. Holding
them in an unparameterized Space reads each file once no matter how many policies are
projected.
Inputs are **external files**: plain CSVs in the model folder's parent directory,
``products/dependance/``, rather than data stored inside the model. The model folder
therefore holds nothing but formulas — no ``_data/``, no IOSpec, no embedded values —
so a diff of the model shows logic changes only. This follows ``annuallife.TradLife_A``;
contrast ``basiclife.BasicTerm_S``, which keeps its inputs *inside* the model through
modelx's IOSpec machinery.
The consequence worth knowing: **the model is not portable on its own.** Copying the
``Dep_FR_S`` folder without its parent's CSVs produces a model that reads and then fails
on first evaluation.
:func:`input_dir` resolves the directory from ``_model.path.parent`` at run time, so
the model works wherever the repository is checked out. Each table has a filename
Reference and a reader Cells:
======================== ============================ ==============================
Reference Cells File
======================== ============================ ==============================
model_point_file model_point_table() model_point_table.csv
mort_table_file mort_table() mort_table.csv
prevalence_file prevalence_table() prevalence_table.csv
severity_share_file severity_share_table() severity_share_table.csv
lapse_table_file lapse_table() lapse_table.csv
cause_mix_file cause_mix_table() cause_mix_table.csv
reduction_file reduction_table() reduction_table.csv
revision_file revision_table() revision_table.csv
======================== ============================ ==============================
**Why the decrement basis is four files and not one.** Nothing in this product's
assumption set comes from a single publication, and the four files are four different
kinds of claim. ``mort_table.csv`` is a **[std]** Gompertz proxy shaped like a French
population table; ``prevalence_table.csv`` holds the three parameters of a **[std]**
logistic fitted to two *sourced* DREES APA prevalence rates per sex;
``severity_share_table.csv`` holds the **[std]** haircuts that turn public APA take-up
on the AGGIR grid into insured prevalence on the contract's own trigger grid, which is
the step no retrieved document supports at all; and ``cause_mix_table.csv`` holds the
**[std]** weights of the three *carence* causes. Keeping them apart keeps their
provenances apart: every one of these files carries a ``provenance`` column, and the
words in it say which of the four kinds each row is.
No table in this library is a copy of a homologated French mortality table. TH 00-02 /
TF 00-02 and TGH05 / TGF05 are cited by name and arrêté in ``technical-notes.md`` and
are not reproduced here.
"""
from modelx.serialize.jsonvalues import *
_formula = None
_bases = []
_allow_none = None
_spaces = []
# ---------------------------------------------------------------------------
# Cells
[docs]
def model_point_table():
"""The model point table, read from *model_point_table.csv*."""
return pd.read_csv( # noqa: F821
input_dir() / model_point_file, index_col="point_id") # noqa: F821
[docs]
def mort_table():
"""The healthy-life mortality rates by sex and age, from *mort_table.csv*.
Annual rates for a life in the **autonomous** state. A **[std]** proxy: a
two-parameter Gompertz force ``mu(x) = B c^x`` with ``B = 5.2321459244e-06`` and
``c = 1.11704543``, fitted to the two **[std]** anchors ``mort_rate(60) = 0.00400``
and ``mort_rate(90) = 0.10500``, shaped like a French female population table with
no sourced value behind either anchor. The male rows are the same force times
**1.60 [std]**, a sex multiple introduced here because ``technical-notes.md``
specifies a female basis only. Rates for the two dependent states are this rate
raised to a power in ``Projection.mort_rate_partial`` and
``Projection.mort_rate_total``, not a separate table: no impaired-life table for
either French dependence state exists in any retrieved source.
The ``provenance`` column says so on every row. This is **not** a copy of
TH 00-02 / TF 00-02 or of TGH05 / TGF05, which are cited by name and arrêté in the
technical notes and are not reproduced by this library.
"""
return pd.read_csv( # noqa: F821
input_dir() / mort_table_file, # noqa: F821
index_col=["sex", "age"]).sort_index()
[docs]
def prevalence_table():
"""The APA-prevalence logistic parameters by sex, from *prevalence_table.csv*.
``prev_ceil``, ``prev_beta`` and ``prev_x_mid`` of
``prev(x) = prev_ceil / (1 + exp(-beta (x - x_mid)))``. The two slope parameters
are pinned to **sourced** DREES rates at end 2023 — 20% of women and 13% of men aged
80 to 89, read at the band midpoint 84.5, and 54% of women and 40% of men from age
90, read at 93 — while ``prev_ceil = 0.90`` is **[std]** and unidentified by the
fit. The ceiling governs the tail, which is where the claims are.
What this table measures is **receipt of the *allocation personnalisée
d'autonomie***: a prevalence, not an incidence, and a public classification on the
AGGIR grid rather than the insurer's. Both conversions are explicit steps
elsewhere — :func:`severity_share_table` for the first and
``Projection.inc_rate_partial`` for the second.
"""
return pd.read_csv( # noqa: F821
input_dir() / prevalence_file, # noqa: F821
index_col=["sex", "param"]).sort_index()
[docs]
def severity_share_table():
"""The public-to-insured severity shares, from *severity_share_table.csv*.
``share_partial`` and ``share_total`` are the fractions of APA prevalence the model
reads as insured *dépendance partielle* and *dépendance totale*, keyed by the
contract's ``trigger_grid``. All three rows are **[std]**. The ``avq5`` row is
bounded by two indirect anchors — the sourced GIR 1-2 share of APA beneficiaries,
34.9%, and the market's ratio of *rentes* in payment to lives covered, about 0.44 —
and the ``avq6`` and ``aggir`` rows are flat factors on it that **no retrieved
document supports at all**. The ``provenance`` column says which is which.
"""
return pd.read_csv( # noqa: F821
input_dir() / severity_share_file, # noqa: F821
index_col="trigger_grid")
[docs]
def lapse_table():
"""The annual lapse rates by policy year, read from *lapse_table.csv*.
Applied to the autonomous ledger alone: a recognised life is exonerated and a
reduced membership is paid up, so neither can lapse for non-payment, and with no
surrender value there is nothing to surrender for. The last row is the terminal
rate — ``Projection.lapse_rate_base`` caps the policy year at the largest year in
the table, so a lifetime projection does not run off the end of it.
"""
return pd.read_csv( # noqa: F821
input_dir() / lapse_table_file, index_col="policy_year") # noqa: F821
[docs]
def cause_mix_table():
"""The **[std]** cause mix weighting the three *carences*, from *cause_mix_table.csv*.
accident 10% / illness other than neurological or psychiatric 55% / neurological or
psychiatric 35%. The three-way *structure* is close to universal across the
retrieved contracts; the **weights** are what a projection needs and no retrieved
document states any. ``Projection.carence_factor`` reads the shares against the
model point's own three *carence* lengths, so a contract with a different menu
changes the model point and not this file.
"""
return pd.read_csv( # noqa: F821
input_dir() / cause_mix_file, index_col="cause") # noqa: F821
[docs]
def reduction_table():
"""The *barème de maintien des garanties*, read from *reduction_table.csv*.
``coefficient`` is the share of the guaranteed *rente totale* a paid-up membership
keeps, by completed years of premiums. The **only published French LTC reduction
scale** retrieved, the CNP Banque de France annexe 2 in force 1 January 2012, whose
own qualifying period is five years; the reference composite applies it from the
eight-year qualifying period of the other retrieved contracts **[std]**, so 25% is
the coefficient at first qualification and the rows at 5, 6 and 7 years are
unreachable on the base cell. The last row applies to 30 years and over.
"""
return pd.read_csv( # noqa: F821
input_dir() / reduction_file, index_col="years_paid") # noqa: F821
[docs]
def revision_table():
"""The scheduled tariff-revision path, read from *revision_table.csv*.
An annual rate by policy year, applied to the premium **on top of** the
*revalorisation des garanties*. A real tariff revision is a management action, not
a projected assumption: the column exists so that the contractual capability is
present and testable, and the shipped path — nil for five years, then 1.5% a year —
is arbitrary inside the 0-10% band the only retrieved cap allows. The last row is
the terminal rate.
"""
return pd.read_csv( # noqa: F821
input_dir() / revision_file, index_col="policy_year") # noqa: F821
# ---------------------------------------------------------------------------
# References
model_point_file = "model_point_table.csv"
mort_table_file = "mort_table.csv"
prevalence_file = "prevalence_table.csv"
severity_share_file = "severity_share_table.csv"
lapse_table_file = "lapse_table.csv"
cause_mix_file = "cause_mix_table.csv"
reduction_file = "reduction_table.csv"
revision_file = "revision_table.csv"
pd = ("Module", "pandas")
