Generating 100K model points

This notebook generates the sample model points ued by the “3. Memory-Optimized Mutiprocess Example” notebook. A DataFrame of 100,000 model points is generated and saved to an Excel file named model_point_table_100K.xlsx in the current directory. The generated model point file can be loaded back by calling pandas’ read_excel function with index_col=0. The DataFrame can be used with CashValue_ME by setting it to model_point_table in the Projection space.

Index and Columns:

• point_id(Index): Model point identifier

• spec_id: Character identifier (‘A’,’B’,’C’ and ‘D’) representing the product specs of the model point. product_spec_table in CashValue_ME.Projection defines the product specs for each 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.

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

• policy_count: The number of policies. Uniformly distributed from 0 to 100.

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

• duration_mth: Months elapsed from the issue til t=0. New business is assumed so 0 for all model points.

• premium_pp: Premium per policy.

• av_pp_init: Initial account value per policy for existing policies. 0 for all model points.

Number of model points:

• 100,000

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 = 'savings'; 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 = 100000

# 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, modified later for whole life): 10, 15, 20
policy_term = rng.integers(low=0, high=3, size=MPCount) * 5 + 10


# Sum Assured (Float): 10,000 - 1,000,000
sum_assured = np.round((1_000_000 - 10_000) * rng.random(size=MPCount) + 10_000, -3)


# Policy Count (Integer): 1
policy_count = np.rint(100 * rng.random(size=MPCount)).astype(int)

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"

# Spec ID
SpedIDs = "ABCD"

spec_ids = np.fromiter(map(lambda i: SpedIDs[i], rng.integers(low=0, high=len(SpedIDs), size=MPCount)), np.dtype('<U1'))
model_point_table.insert(0, 'spec_id', spec_ids)

whole_life = (model_point_table['spec_id'] == 'C')  | (model_point_table['spec_id'] == 'D')

# Modify Policy Term

model_point_table['policy_term'].mask(whole_life, 9999, inplace=True)

# Set Duration to 0

model_point_table['duration_mth'] = 0

# Premium per Plicy (by adjusting size between single premium(A and B) and level premium(C and D) policies)

premium_pp = np.ceil(model_point_table['sum_assured'] / (80 - model_point_table['age_at_entry']) / 12 / 100) * 100

model_point_table.insert(len(model_point_table.columns), 'premium_pp', model_point_table['sum_assured'])
model_point_table['premium_pp'].mask(whole_life, premium_pp, inplace=True)

# Ininital Account Value per Policy

model_point_table['av_pp_init'] = 0

model_point_table

	spec_id	age_at_entry	sex	policy_term	policy_count	sum_assured	duration_mth	premium_pp	av_pp_init
policy_id
1	A	47	M	20	22	804000.0	0	804000.0	0
2	C	29	F	9999	75	519000.0	0	900.0	0
3	A	51	F	10	5	409000.0	0	409000.0	0
4	B	32	M	15	60	128000.0	0	128000.0	0
5	D	28	M	9999	45	698000.0	0	1200.0	0
...	...	...	...	...	...	...	...	...	...
99996	A	21	M	10	34	152000.0	0	152000.0	0
99997	D	24	F	9999	53	928000.0	0	1400.0	0
99998	B	46	F	15	72	662000.0	0	662000.0	0
99999	A	46	M	15	36	583000.0	0	583000.0	0
100000	B	35	M	15	3	638000.0	0	638000.0	0

100000 rows × 9 columns

model_point_table.to_excel('model_point_table_100K.xlsx')