Source code for uklib.products.whole_of_life.WOL_UK_S.Data
# 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 three input CSVs are read here, **once per model**, and referenced from
:mod:`~.WOL_UK_S.Projection` as ``data``. :mod:`~.WOL_UK_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/whole_of_life/``, 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
``WOL_UK_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
lapse_table_file lapse_table() lapse_table.csv
====================== ========================== ==============================
Both the mortality and the lapse table are keyed by the **cell** as well as by the usual
rating factors, because the two cells of this product do not share a basis and must not
be given each other's. The underwritten cell restores select experience through full
underwriting and takes an assured-lives shape; the guaranteed-acceptance cell cannot be
better than the population and self-selects worse, so it takes a population shape with an
anti-selection loading. The FCA's price differential between the two designs — £71.73
against £8.10 per £1,000 of cover — is the scale of the error a basis mix-up produces.
"""
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 annual mortality rates, read from *mort_table.csv*.
Keyed by basis (``population`` or ``assured``), sex, smoker status and age last
birthday, and capped at 1. Both bases are **[std]** proxies: the ``population``
rates are shaped like the ONS national life tables and anchored so that the O50
cell's loaded rate is the notes' walk-through basis exactly, and the ``assured``
rates are shaped like the "00" Series permanent assurance tables. Neither is a
published table, and the file's ``provenance`` column says which cells are anchors
and which come from a sex or smoker factor. Sorted on read, because
``Projection.mort_rate`` indexes into it.
"""
return pd.read_csv( # noqa: F821
input_dir() / mort_table_file, # noqa: F821
index_col=["basis", "sex", "smoker", "age"]).sort_index()
[docs]
def lapse_table():
"""The annual lapse rates by cell and policy year, from *lapse_table.csv*.
``policy_year`` is the contractual 1-based label (1 for the first policy year), not
the projection's 0-based month index; ``Projection.lapse_rate_base`` maps into it
through ``policy_year(t) = t // 12 + 1``, so the file's keys are left as written.
The two cells carry different tables: the guaranteed-acceptance one lapses faster
early, on affordability attrition. Both are **[std]** drafting constructions - no
public UK whole of life lapse study was retrieved - and on a product with no
surrender value they are the single largest lever on the liability.
"""
return pd.read_csv( # noqa: F821
input_dir() / lapse_table_file, # noqa: F821
index_col=["cell", "policy_year"]).sort_index()
# ---------------------------------------------------------------------------
# References
model_point_file = "model_point_table.csv"
mort_table_file = "mort_table.csv"
lapse_table_file = "lapse_table.csv"
pd = ("Module", "pandas")
