ins-pricing 0.3.3

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 (SHAP, permutation importance)
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 & GPU installation notes

• PyTorch (CPU/GPU/MPS): Install the correct PyTorch build for your platform/GPU first (CUDA on Linux/Windows, ROCm on supported AMD platforms, or MPS on Apple Silicon). Then install the optional extras you need (e.g., bayesopt, explain, or gnn). This avoids pip pulling a mismatched wheel.
• Torch Geometric (GNN): torch-geometric often requires platform-specific wheels (e.g., torch-scatter, torch-sparse). Follow the official PyG installation instructions for your CUDA/ROCm/CPU environment, then install ins_pricing[gnn].
• Multi-GPU: Training code will use CUDA when available and can enable multi-GPU via torch.distributed/DataParallel where supported. On Windows, CUDA DDP is not supported and will fall back to single-GPU or DataParallel where possible.

Requirements

• Python >= 3.9
• Core dependencies: numpy >= 1.20, pandas >= 1.4

License

Proprietary