Skip to main content

Umbrella package for the Electric Barometer ecosystem (metrics, evaluation, features, adapters, and related tooling).

Project description

Electric Barometer

License: BSD-3-Clause Python Versions Project Status

Electric Barometer is a modular, cost-aware forecasting evaluation framework designed for operational decision-making. It provides a structured way to evaluate, compare, and select forecasts when error costs are asymmetric and operational consequences matter.

Rather than delivering a single monolithic library, Electric Barometer is intentionally organized as a small ecosystem of focused packages, each with a clear responsibility.

This repository serves as the umbrella distribution and conceptual entry point for the Electric Barometer ecosystem.


The Electric Barometer Ecosystem

Electric Barometer is composed of several interoperable packages:

  • eb-metrics
    Defines individual forecast error and service metrics, including cost-asymmetric measures such as Cost-Weighted Service Loss (CWSL), Forecast Readiness Score (FRS), and related primitives.

  • eb-evaluation
    Provides DataFrame-first utilities for applying metrics across entities, groups, hierarchies, and time windows. This layer handles evaluation, comparison, and selection logic while delegating metric math to eb-metrics.

  • eb-adapters
    Normalizes interfaces for external forecasting and regression libraries so they can be evaluated consistently. Adapters expose a common .fit / .predict contract for heterogeneous models.

  • eb-examples
    Contains worked examples, notebooks, and practical demonstrations showing how the Electric Barometer ecosystem is used end-to-end in real scenarios.

  • eb-papers
    The source of truth for conceptual definitions, theoretical foundations, and methodological rationale behind Electric Barometer metrics and frameworks.

Each package is versioned, tested, and documented independently, but designed to work together seamlessly.


What This Repository Provides

This electric-barometer repository:

  • Acts as the canonical entry point to the ecosystem
  • Provides a single install surface for core Electric Barometer functionality
  • Establishes the conceptual map of the project
  • Ensures compatible dependency resolution across subpackages

It intentionally contains minimal implementation code.


Installation

Install the Electric Barometer umbrella package via pip:

pip install electric-barometer

This installs the core dependencies required to work with Electric Barometer metrics. Additional functionality is provided by installing the underlying packages directly (e.g., eb-evaluation, eb-adapters) or via future optional extras.

For development:

pip install -e .

Design Philosophy

Electric Barometer is built around a few core principles:

  • Separation of concerns
    Metric definitions, evaluation logic, and model interfaces live in separate packages.

  • Cost-aware evaluation
    Forecast accuracy is evaluated in terms of operational impact, not symmetric error alone.

  • Operational realism
    Metrics and frameworks are designed for environments where underbuild and overbuild have different consequences.

  • Composable tooling
    Users can adopt only the layers they need without committing to a monolith.


Examples and Tutorials

Examples, notebooks, and applied workflows are maintained in the separate eb-examples repository.

This repository intentionally avoids embedding example code to keep the core packages lean and focused.


Documentation

Unified documentation for the Electric Barometer ecosystem is available at:

https://economistician.github.io/eb-docs/

Documentation is generated directly from source code docstrings and kept consistent across packages.


Status

Electric Barometer is under active development. Public APIs may evolve prior to the first stable release.


Authorship and Stewardship

The Electric Barometer ecosystem is designed and maintained by
Kyle Corrie under the Economistician moniker.

The project reflects applied research and production experience in forecasting, operations research, and cost-asymmetric decision systems within large-scale operational environments.

For questions, collaboration, or research inquiries:

Conceptual foundations and formal methodology are documented in the companion research repository eb-papers.


License

This project is licensed under the BSD 3-Clause License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

electric_barometer-0.2.0.tar.gz (5.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

electric_barometer-0.2.0-py3-none-any.whl (5.4 kB view details)

Uploaded Python 3

File details

Details for the file electric_barometer-0.2.0.tar.gz.

File metadata

  • Download URL: electric_barometer-0.2.0.tar.gz
  • Upload date:
  • Size: 5.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.3

File hashes

Hashes for electric_barometer-0.2.0.tar.gz
Algorithm Hash digest
SHA256 eee001375bfedd834ca706e84d764bd7be81710454b30cddba1f0e737f122ae0
MD5 2a72c0636c9817ca7fcb4f4feac01912
BLAKE2b-256 47b0eaed24573ee2aa3590f77c7220d821f4e30140186dce50d4625f9a3dfdf0

See more details on using hashes here.

File details

Details for the file electric_barometer-0.2.0-py3-none-any.whl.

File metadata

File hashes

Hashes for electric_barometer-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 07b3ef7e375f2e352e831ec0718f85061c9952e523e8d3633109702bd852dfd3
MD5 6c04952fa22084ae994352e98f8e3bfa
BLAKE2b-256 fd7a2bec6b23f85e6eb7076edf579ed08734f964cb0b073d703beb973840a156

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page