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Core engine for multidimensional evaluation under explicit policy assumptions.

Project description

multidimensional-evaluation-engine

PyPI version Latest Release Docs CI Status Deploy-Docs Check Links Dependabot Python 3.14+ MIT

A domain-neutral engine for multidimensional evaluation under explicit policy assumptions.

What the Engine Does

The engine:

  • applies a configurable policy
  • evaluates candidate configurations
  • computes scores using rule-based mappings
  • evaluates constraint-based admissibility
  • derives interpretation indicators from score thresholds
  • supports structured comparison across alternative designs

The system is designed to make assumptions explicit and results inspectable.

Important Note

This project does not advocate for a specific solution.

It provides a structured way to examine:

  • how design choices affect outcomes
  • where tradeoffs become significant
  • how governance assumptions shape results

This Project

This project provides a reusable framework for multidimensional evaluation under explicit assumptions and constraints. It:

  • supports policy-driven evaluation across multiple domains
  • represents inputs as typed factors (binary, numeric, categorical)
  • applies constraint rules and score rules defined in policy
  • separates input structure, policy logic, and evaluation

The goal is to provide a stable core that can support multiple exploratory systems built on a shared evaluation model.

Contribution

The contribution is the engine for structured multidimensional evaluation, not the specific values used in any given scenario.

  • Factors and their structure are explicitly defined
  • Scoring and constraints are policy-driven
  • Assumptions are explicit and inspectable
  • Results are comparative and scenario-dependent
  • The core logic is domain-neutral

This project does not determine outcomes or recommend decisions. It provides a way to examine how different assumptions and constraints shape results.

Working Files

Working files are found in these areas:

  • docs/ - documentation and examples
  • src/ - implementation

Capabilities

  • Loads policy definitions (factor specs, constraint rules, score rules)
  • Evaluates candidates using typed factor values
  • Computes score profiles, admissibility, and interpretation indicators
  • Supports reusable integration into domain-specific explorer systems

Command Reference

Show command reference

In a machine terminal (open in your Repos folder)

After you get a copy of this repo in your own GitHub account, open a machine terminal in your Repos folder:

# Replace username with YOUR GitHub username.
git clone https://github.com/username/multidimensional-evaluation-engine

cd multidimensional-evaluation-engine
code .

In a VS Code terminal

# Set Up the Environment
uv self update
uv python pin 3.14
uv sync --extra dev --extra docs --upgrade
uvx pre-commit install

# Local format + lint
uv run ruff format --check .
uv run ruff check .

# Pre-commit (enforce repo rules)
git add -A
uvx pre-commit run --all-files
# repeat if changes were made
git add -A
uvx pre-commit run --all-files

# Static + security + dependency checks
uv run validate-pyproject pyproject.toml
uv run deptry .
uv run bandit -c pyproject.toml -r src
uv run pyright

# Tests (after static checks pass)
uv run pytest --cov=src --cov-report=term-missing

# Docs build (after everything passes)
uv run zensical build

# Commit and push
git add -A
git commit -m "update"
git push -u origin main

# Reinstall + sanity checks (post-push validation)
uv sync --reinstall

uv run python -c "import multidimensional_evaluation_engine; print(multidimensional_evaluation_engine.__version__)"
uv run python -c "from multidimensional_evaluation_engine.evaluation.evaluator import evaluate_candidate; print(evaluate_candidate)"

# Build artifacts (verify release)
uv build

Annotations

ANNOTATIONS.md

Citation

CITATION.cff

License

MIT

SE Manifest

SE_MANIFEST.md

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