ins-pricing 0.2.2

Reusable modelling, pricing, governance, and reporting utilities.

Ins-Pricing Overview

This repository contains risk modeling and optimization notebooks, scripts, and a reusable training framework. The main module is ins_pricing/modelling/bayesopt.

Top-level structure

• Auto Info/: vehicle info crawling, preprocessing, and embedding experiments
• GLM and LGB/: GLM/LightGBM modeling experiments
• OpenAI/: OpenAI notebook prototypes
• Python Code/: runnable scripts and utilities
• others/: temporary or miscellaneous notebooks
• ins_pricing/: reusable training framework and CLI tools (BayesOpt subpackage)
• user_packages legacy/: historical snapshot

Note: ins_pricing/modelling/examples/ is kept in the repo only and is not shipped in the PyPI package.

Quickstart

Run the following commands from the repo root:

python -m venv .venv
source .venv/bin/activate  # Windows: .\\.venv\\Scripts\\activate
pip install pandas scikit-learn lightgbm seaborn matplotlib

Start notebooks:

jupyter lab

BayesOpt entry points

• CLI batch training: python ins_pricing/modelling/cli/BayesOpt_entry.py --config-json <path>
• Incremental training: python ins_pricing/modelling/cli/BayesOpt_incremental.py --config-json <path>
• Python API: from ins_pricing.modelling import BayesOptModel

Tests

pytest -q

Data and outputs

• Put shared data under data/ (create it if needed).
• Training outputs are written to plot/, Results/, and model/ by default.
• Keep secrets and large files outside the repo and use environment variables or .env.