Source code for uklib.products.critical_illness.CI_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:`~.CI_UK_S.Projection` as ``data``. :mod:`~.CI_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/critical_illness/``, 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
``CI_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
ci_rate_file ci_rate_table() ci_rate_table.csv
lapse_table_file lapse_table() lapse_table.csv
====================== ========================== ==============================
``ci_rate_table.csv`` holds **pivot ages only** — 40, 45, 50, 55, 60, 65 — because that
is the form the technical notes give the basis in, together with the rule that
intermediate ages are interpolated log-linearly. The interpolation therefore lives in
``Projection.pivot_interp`` rather than being baked into a pre-expanded file, and
swapping in a licensed AC04 or "16" Series basis means replacing a 24-row table, not a
generated one.
"""
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 ci_rate_table():
"""The CI diagnosis and mortality pivot rates, from *ci_rate_table.csv*.
Annual rates at the pivot ages 40, 45, 50, 55, 60 and 65 by sex and smoker status.
``i_ci`` is the first-diagnosis rate for a listed condition including total and
permanent disability; ``q_d`` is best-estimate mortality. Both are **[std]**
proxies - the CMI accelerated-CI tables are subscriber-restricted - and the file's
``provenance`` column says which cells came from the notes and which from a
sex/smoker factor.
Sorted on read: ``Projection.pivot_interp`` slices the frame by (sex, smoker), and
pandas warns about indexing past the lexsort depth of an unsorted MultiIndex.
"""
return pd.read_csv( # noqa: F821
input_dir() / ci_rate_file, # noqa: F821
index_col=["sex", "smoker", "age"]).sort_index()
[docs]
def lapse_table():
"""The lapse rates by policy year, read from *lapse_table.csv*."""
return pd.read_csv( # noqa: F821
input_dir() / lapse_table_file, index_col="policy_year") # noqa: F821
# ---------------------------------------------------------------------------
# References
model_point_file = "model_point_table.csv"
ci_rate_file = "ci_rate_table.csv"
lapse_table_file = "lapse_table.csv"
pd = ("Module", "pandas")
