Generating model points

This notebook generates the sample model points used by the BasicTerm_S and BasicTerm_M models, by using random numbers. BasicTerm_S and BasicTerm_M are new business models, so no duration information is included.

Columns:

• point_id: Model point identifier

• age_at_entry: Issue age. The samples are distributed uniformly from 20 to 59.

• sex: “M” or “F” to indicate policy holder’s sex. Not used by default.

• policy_term: Policy term in years. The samples are evenly distriubted among 10, 15 and 20.

• policy_count: The number of policies. Not used by default.

• sum_assured: Sum assured. The samples are uniformly distributed from 10,000 to 1,000,000.

Number of model points:

• 10000

Click the badge below to run this notebook online on Google Colab. You need a Google account and need to be logged in to it to run this notebook on Google Colab.

The next code cell below is relevant only when you run this notebook on Google Colab. It installs lifelib and creates a copy of the library for this notebook.

import sys, os

if 'google.colab' in sys.modules:
    lib = 'basiclife'; lib_dir = '/content/'+ lib
    if not os.path.exists(lib_dir):
        !pip install lifelib
        import lifelib; lifelib.create(lib, lib_dir)

    %cd $lib_dir

import numpy as np
from numpy.random import default_rng  # Requires NumPy 1.17 or newer

rng = default_rng(12345)

# Number of Model Points
MPCount = 10000

# Issue Age (Integer): 20 - 59 year old

age_at_entry = rng.integers(low=20, high=60, size=MPCount)

# Sex (Char)

Sex = [
    "M",
    "F"
]

sex = np.fromiter(map(lambda i: Sex[i], rng.integers(low=0, high=len(Sex), size=MPCount)), np.dtype('<U1'))

# Policy Term (Integer): 10, 15, 20

policy_term = rng.integers(low=0, high=3, size=MPCount) * 5 + 10

# Policy Count (Integer): 1

policy_count = [1] * MPCount

# Sum Assured (Float): 10000 - 1000000

sum_assured = np.round((1000000 - 10000) * rng.random(size=MPCount) + 10000, -3)

import pandas as pd

attrs = [
    "age_at_entry",
    "sex",
    "policy_term",
    "policy_count",
    "sum_assured"
]

data = [
    age_at_entry,
    sex,
    policy_term,
    policy_count,
    sum_assured
]

model_point_table = pd.DataFrame(dict(zip(attrs, data)), index=range(1, MPCount+1))
model_point_table.index.name = "policy_id"
model_point_table

	age_at_entry	sex	policy_term	policy_count	sum_assured
policy_id
1	47	M	10	1	622000.0
2	29	M	20	1	752000.0
3	51	F	10	1	799000.0
4	32	F	20	1	422000.0
5	28	M	15	1	605000.0
...	...	...	...	...	...
9996	47	M	20	1	827000.0
9997	30	M	15	1	826000.0
9998	45	F	20	1	783000.0
9999	39	M	20	1	302000.0
10000	22	F	15	1	576000.0

10000 rows × 5 columns

model_point_table.to_excel("model_point_table.xlsx")