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Mohd Kaif
a326c7d3bd
* fix: replace invalid Mintlify theme 'venus' with 'mint' * docs: replace em dashes with colons across all docs files * fix: strip UTF-8 BOM from all docs files (broke frontmatter detection)
75 lines
3.0 KiB
Markdown
75 lines
3.0 KiB
Markdown
---
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title: "Evals Module"
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description: "Evaluation framework for measuring Knowledge Graph quality, extraction accuracy, and pipeline performance: coming soon."
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icon: "chart-line"
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---
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**`semantica.evals`** is planned as a comprehensive evaluation framework for measuring **extraction accuracy, graph quality, and pipeline performance**.
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<Warning>
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**`semantica.evals` is not yet implemented.** The module is a placeholder with `__all__ = []`. No classes or functions are available for import. This page describes the planned API only.
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</Warning>
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## Planned Features
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When released, `semantica.evals` will provide:
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| Planned Class | Role |
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| :--- | :--- |
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| `KGEvaluator` | Completeness, consistency, schema compliance, coverage, and orphan node detection |
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| `ExtractionEvaluator` | NER precision / recall / F1 and relation extraction metrics against gold datasets |
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| `PipelineBenchmark` | Throughput (docs/sec), per-step latency, peak memory, and error rate |
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| `RegressionTracker` | Record runs and compare metrics across commits or config changes |
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| `EvalReport` | Structured report: `{scores, regressions, recommendations}` |
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| `DeduplicationEvaluator` | Merge precision, false positive / false negative rates |
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| `ReasoningEvaluator` | Inference accuracy, rule coverage, and derivation depth |
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## Current Workaround
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Until `semantica.evals` ships, use `semantica.ontology.OntologyEvaluator` for ontology quality metrics:
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```python
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from semantica.ontology import OntologyEvaluator
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evaluator = OntologyEvaluator()
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# evaluate_ontology takes the ontology dict only
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result = evaluator.evaluate_ontology(ontology)
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print("Coverage: ", result.coverage_score)
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print("Completeness:", result.completeness_score)
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print("Gaps: ", result.gaps)
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print("Suggestions: ", result.suggestions)
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# Full report with class granularity and relation completeness
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report = evaluator.generate_report(ontology)
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print("Coverage score: ", report["evaluation"]["coverage_score"])
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print("Completeness score:", report["evaluation"]["completeness_score"])
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print("Relation coverage: ", report["relation_completeness"]["relation_coverage"])
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```
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`EvaluationResult` fields returned by `evaluate_ontology()`:
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| Field | Type | Description |
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| :----- | :---- | :----------- |
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| `coverage_score` | `float` | Fraction of competency questions answerable by the ontology |
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| `completeness_score` | `float` | Average of class and property completeness scores |
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| `gaps` | `List[str]` | Identified gaps in coverage |
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| `suggestions` | `List[str]` | Improvement suggestions |
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| `metrics` | `dict` | Detailed sub-metrics |
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<CardGroup cols={2}>
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<Card title="Semantic Extract" icon="magnifying-glass" href="semantic_extract">
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Extraction module.
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</Card>
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<Card title="Knowledge Graph" icon="diagram-project" href="kg">
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Graph quality assessment.
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</Card>
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<Card title="Pipeline" icon="gear" href="pipeline">
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Pipeline performance metrics.
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</Card>
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<Card title="Ontology Evaluator" icon="sitemap" href="ontology">
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Available now for ontology quality metrics.
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</Card>
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</CardGroup>
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