ins-pricing 0.7.1

Reusable modelling, pricing, governance, and reporting utilities.

Insurance-Pricing

A reusable toolkit for insurance modeling, pricing, governance, and reporting.

Overview

Insurance-Pricing (ins_pricing) is an enterprise-grade Python library designed for machine learning model training, pricing calculations, and model governance workflows in the insurance industry.

Core Modules

Module	Description
modelling	ML model training (GLM, XGBoost, ResNet, FT-Transformer, GNN) and model interpretability
pricing	Factor table construction, numeric binning, premium calibration, exposure calculation, PSI monitoring
production	Model prediction, batch scoring, data drift detection, production metrics monitoring
governance	Model registry, version management, approval workflows, audit logging
reporting	Report generation (Markdown format), report scheduling
utils	Data validation, performance profiling, device management, logging configuration

Quick Start

# Model training with Bayesian optimization
from ins_pricing import bayesopt as ropt

model = ropt.BayesOptModel(
    train_data, test_data,
    model_name='my_model',
    resp_nme='target',
    weight_nme='weight',
    factor_nmes=feature_list,
    cate_list=categorical_features,
)
model.bayesopt_xgb(max_evals=100)      # Train XGBoost
model.bayesopt_resnet(max_evals=50)    # Train ResNet
model.bayesopt_ft(max_evals=50)        # Train FT-Transformer

# Pricing: build factor table
from ins_pricing.pricing import build_factor_table
factors = build_factor_table(
    df,
    factor_col='age_band',
    loss_col='claim_amount',
    exposure_col='exposure',
)

# Production: batch scoring
from ins_pricing.production import batch_score
scores = batch_score(model.trainers['xgb'].predict, df)

# Model governance
from ins_pricing.governance import ModelRegistry
registry = ModelRegistry('models.json')
registry.register(model_name, version, metrics=metrics)

Project Structure

ins_pricing/
  cli/                    # Command-line entry points
  modelling/
    core/bayesopt/         # ML model training core
    explain/               # Model interpretability
    plotting/              # Model visualization
  pricing/                 # Insurance pricing module
  production/              # Production deployment module
  governance/              # Model governance
  reporting/               # Report generation
  utils/                   # Utilities
  tests/                   # Test suite

Installation

# Basic installation
pip install ins_pricing

# Full installation (all optional dependencies)
pip install ins_pricing[all]

# Install specific extras
pip install ins_pricing[bayesopt]    # Model training
pip install ins_pricing[explain]     # Model explanation
pip install ins_pricing[plotting]    # Visualization
pip install ins_pricing[gnn]         # Graph neural networks

Multi-platform and GPU notes

• Install the correct PyTorch build for your platform/GPU before installing extras.
• Torch Geometric requires platform-specific wheels; follow the official PyG install guide.
• Multi-GPU uses torch.distributed/DataParallel where supported; Windows disables CUDA DDP.

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PyPI Upload (scripts)

This repo includes upload scripts for Windows and Linux/macOS.

Windows

set TWINE_PASSWORD=your_pypi_token_here
python -m build
upload_to_pypi.bat

Linux / macOS

chmod +x upload_to_pypi.sh
export TWINE_PASSWORD='your_pypi_token_here'
python -m build
./upload_to_pypi.sh

Makefile (if make is available)

make build
make upload

Tips

• Never commit tokens to version control.
• Use environment variables or secret managers to store credentials.
• Test with TestPyPI before publishing when needed.