wehub-resource-sync
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
from pathlib import Path
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import pytest
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from plugin_eval.engine import EvalEngine
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from plugin_eval.models import Depth, EvalConfig, PluginEvalResult
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class TestEvalEngine:
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def test_quick_eval_skill(self, sample_skill_dir: Path):
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config = EvalConfig(depth=Depth.QUICK)
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engine = EvalEngine(config)
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result = engine.evaluate_skill(sample_skill_dir)
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assert isinstance(result, PluginEvalResult)
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assert len(result.layers) == 1
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assert result.layers[0].layer == "static"
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assert result.composite is not None
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assert result.composite.confidence_label == "Estimated"
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def test_quick_eval_plugin(self, sample_plugin_dir: Path):
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config = EvalConfig(depth=Depth.QUICK)
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engine = EvalEngine(config)
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result = engine.evaluate_plugin(sample_plugin_dir)
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assert isinstance(result, PluginEvalResult)
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assert result.composite.score > 0
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def test_composite_score_within_bounds(self, sample_skill_dir: Path):
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config = EvalConfig(depth=Depth.QUICK)
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engine = EvalEngine(config)
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result = engine.evaluate_skill(sample_skill_dir)
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assert 0 <= result.composite.score <= 100
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def test_layer_blend_renormalization(self):
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"""When only L1 is available, L1 weights should renormalize to 1.0."""
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engine = EvalEngine(EvalConfig(depth=Depth.QUICK))
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blended = engine._blend_layer_scores(
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static_scores={"triggering_accuracy": 0.9, "orchestration_fitness": 0.8},
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judge_scores=None,
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mc_scores=None,
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)
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assert blended["triggering_accuracy"] > 0
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assert blended["orchestration_fitness"] > 0
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