The Data Space

Input data shared by every by-policy projection.

The three input CSVs are read here, once per model, and referenced from Projection as data. 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.

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.

Cells Descriptions

input_dir()

The directory holding the input CSVs: the model folder’s parent.

Inputs are external files, not data stored inside the model, so the model folder is pure formulas. The path is resolved at run time from where the model was read, following annuallife.TradLife_A.

model_point_table()

The model point table, read from model_point_table.csv.

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.

lapse_table()

The lapse rates by policy year, read from lapse_table.csv.