The Judge/Scoring engine for XRTM.
Project description
xrtm-eval
The Judge for XRTM.
xrtm-eval is the rigorous scoring engine used to grade probabilistic forecasts. It operates independently of the inference engine to ensure objective evaluation.
Part of the XRTM Ecosystem
Layer 4: xrtm-train → (imports all)
Layer 3: xrtm-forecast → (imports eval, data)
Layer 2: xrtm-eval → (imports data) ← YOU ARE HERE
Layer 1: xrtm-data → (zero dependencies)
xrtm-eval provides scoring metrics AND trust primitives used by the forecast engine.
Installation
pip install xrtm-eval
Core Primitives
1. Brier Score Breakdown
We do not use simple accuracy. We use the Brier Score, decomposed into its three component terms:
- Reliability: How well do the predicted probabilities match observed frequencies?
- Resolution: How well does the forecast distinguish between events that happen and those that don't?
- Uncertainty: The inherent difficulty of the problem.
from xrtm.eval import BrierScoreEvaluator
evaluator = BrierScoreEvaluator()
score = evaluator.score(prediction=0.7, ground_truth=1)
# score = (0.7 - 1.0)^2 = 0.09
2. Expected Calibration Error (ECE)
Use the ExpectedCalibrationErrorEvaluator to measure the gap between confidence and accuracy across bin buckets.
3. Epistemic Trust Primitives (v0.1.1+)
xrtm-eval now includes trust scoring infrastructure:
from xrtm.eval.core.epistemics import IntegrityGuardian, SourceTrustRegistry
registry = SourceTrustRegistry()
guardian = IntegrityGuardian(registry)
Project Structure
src/xrtm/eval/
├── core/ # Interfaces & Schemas
│ ├── eval/ # Evaluator protocol, EvaluationResult
│ ├── epistemics.py # Trust primitives (SourceTrustRegistry)
│ └── schemas/ # ForecastResolution
├── kit/ # Composable evaluator implementations
│ └── eval/metrics.py # BrierScoreEvaluator, ECE
└── providers/ # External evaluation services (future)
Development
Prerequisites:
# Install dependencies
uv sync
# Run tests
uv run pytest
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