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Browser-based GLM workbench for actuarial pricing

Project description

Atelier

Browser-based GLM workbench for actuarial pricing

Build, fit, diagnose, and iterate on Generalized Linear Models - without leaving your browser.

Python 3.13+ License: EPL-2.0 Powered by rustystats

Atelier Screenshot


Why Atelier?

Traditional actuarial pricing tools like Emblem are expensive, opaque, and tied to legacy platforms. Atelier is a modern, open-source alternative that wraps rustystats - a high-performance Rust-backed GLM engine - in a clean, interactive UI. It runs locally, stores everything on your machine, and follows the same explore-build-fit-iterate workflow actuaries already know.

Installation

uv add atel
# or
pip install atel

Installs everything - backend, frontend, engine. No separate build steps.

Quick start

atel                  # starts server, opens browser
atel --port 9000      # custom port
atel --no-browser     # start server only

The atelier command works too - atel is just shorter.


How it works

Workflow

Atelier follows the standard actuarial modelling workflow:

  1. Upload - drag-and-drop a CSV or Parquet file, column types are auto-detected
  2. Configure - select the response variable, GLM family, link function, offset, weights, and train/test split
  3. Explore - pre-fit analysis runs automatically: response distribution, score tests ranking every candidate factor by expected deviance contribution, and a null (intercept-only) baseline model
  4. Build - add terms from the factor sidebar: right-click any factor to choose categorical, linear, spline, target encoding, or other term types
  5. Fit - hit fit, review the results: coefficient table, A/E charts, lift, calibration, VIF, and model diagnostics
  6. Iterate - modify terms and re-fit. Every fit is auto-versioned so you can compare metrics across iterations and restore any previous version

Architecture

┌─────────────────────────────────────────────┐
│  Browser (React 19 + Tailwind + shadcn/ui)  │
└──────────────────┬──────────────────────────┘
                   │ HTTP/JSON
┌──────────────────▼──────────────────────────┐
│  FastAPI backend                             │
│  ├── /api/datasets   upload, validate        │
│  ├── /api/explore    EDA + null model        │
│  ├── /api/fit        GLM fitting             │
│  ├── /api/models     save, history, restore  │
│  └── /api/projects   project CRUD            │
├──────────────────────────────────────────────┤
│  rustystats          Rust GLM engine         │
├──────────────────────────────────────────────┤
│  SQLite (async)      projects, models, specs │
└──────────────────────────────────────────────┘

All data stays local at ~/.atelier/ - the database, uploaded datasets, and serialized models.


Features

Model building

  • 8 GLM families - Gaussian, Poisson, Binomial, Gamma, Tweedie, Quasi-Poisson, Quasi-Binomial, Negative Binomial
  • Rich term types - categorical, linear, B-splines, natural splines, target encoding, frequency encoding, expressions
  • Monotonic constraints - enforce increasing/decreasing effects on splines and linear terms
  • Interactions - standard product terms, target-encoded interactions, frequency-encoded interactions
  • Regularization - Ridge, Lasso, Elastic Net with cross-validated alpha selection
  • Train/test split - holdout validation with stratified splitting

Diagnostics

  • Factor-level A/E - actual vs expected charts for every factor, fitted or not
  • Score tests - chi-squared significance for candidate factors before fitting
  • Lift charts - Gini, AUC, KS statistics with decile breakdown
  • Calibration - Hosmer-Lemeshow test, decile calibration with confidence intervals
  • Residual analysis - deviance, Pearson, and working residuals
  • VIF & multicollinearity - variance inflation factors with severity coloring
  • Model comparison - side-by-side train/test metrics against a base model

Data exploration

  • Pre-fit analysis - response distribution, zero inflation, overdispersion detection
  • Correlation matrix - numeric correlations and Cramer's V for categoricals
  • Interaction detection - greedy residual-based search for potential interactions

Version control

  • Auto-versioning - every fit is saved as a new version with full spec, coefficients, and diagnostics
  • Change tracking - history panel shows terms added, removed, or modified between versions
  • Restore - click any version to restore its terms and results, then continue iterating

License

Eclipse Public License 2.0

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