Tables¤
Heavylight is design to be modular, you can pick and choose which functionality you use.
A core feature of actuarial models is the use of a variety of assumption tables, for example yield curves, mortality tables, lapse rates and expenses tables.
Heavylight includes the Table class which provides both individual and vectorised lookups, and an opinionated format for loading tables from pandas dataframes, csv files and excel files.
from heavylight import Table
import pandas as pd
import numpy as np
# typically you would import the table from a csv or other source
df = pd.DataFrame(
{'age|int': np.arange(18, 65)}
)
df['values'] = df['age|int'] * 0.01
tab = Table(df)
# we can query a single value
print(tab[20]) # prints(0.20)
# we can query multiple values at once using numpy arrays.
rng = np.random.default_rng(seed=42) # set up random number generator
ages = rng.integers(low=18, high=65, size=100_000, endpoint=True) # sample 100k ages
vals = tab[ages] # query the table and store values
Defining Column Types¤
Table determines the type of key column based on the column name suffix, the following are supported:
|int: integers (...0, 1, 2, 3...), can start and end anywhere, but must be consecutive|int_bound: as|intbut any values are constrained to the lowest and highest values.|int_cat: integer are interpreted as categories, e.g. frequencies of 1, 3, 6, 12 months.|str: keys are interpreted as strings, e.g. 'M' and 'F'|band: key is numeric and treated as the upper bound on a lookup.
Mult-factor tables¤
Tables aren't limited to a single key, and keys can take any of the above types.
For examples, if a table tab2 had headers age|int and sex|str, to look up the value for age 20 and sex F:
This also works with by passing in numpy arrays of keys.