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52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
"""Unit tests for HuggingFaceReranker score normalization.
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These exercise the pure ``_normalize_scores`` helper directly, so they do not
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require ``transformers`` / ``torch`` to be installed.
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"""
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import math
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import pytest
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from mem0.reranker.huggingface_reranker import HuggingFaceReranker
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def _sigmoid(x):
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return 1.0 / (1.0 + math.exp(-x))
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class TestHuggingFaceNormalizeScores:
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def test_logits_mapped_via_sigmoid(self):
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scores = HuggingFaceReranker._normalize_scores([2.0, 8.0, 5.0])
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assert scores == pytest.approx([_sigmoid(2.0), _sigmoid(8.0), _sigmoid(5.0)])
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def test_output_bounded_between_zero_and_one(self):
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for s in HuggingFaceReranker._normalize_scores([-12.0, -1.0, 0.0, 3.0, 15.0]):
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assert 0.0 <= s <= 1.0
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def test_sigmoid_preserves_ranking_order(self):
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raw = [1.0, -4.0, 9.0, 2.5]
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normalized = HuggingFaceReranker._normalize_scores(raw)
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# argsort of raw and normalized must match — sigmoid is monotonic.
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assert sorted(range(len(raw)), key=lambda i: raw[i]) == sorted(
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range(len(normalized)), key=lambda i: normalized[i]
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)
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def test_single_score_not_collapsed_to_zero(self):
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# Regression: a lone document used to normalize to ~0.0 under min-max.
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# A positive logit must now yield a clearly-relevant score (> 0.5).
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(score,) = HuggingFaceReranker._normalize_scores([4.2])
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assert score == pytest.approx(_sigmoid(4.2))
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assert score > 0.5
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def test_tied_scores_not_collapsed_to_zero(self):
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# Regression: tied candidates all collapsed to ~0.0 under min-max.
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scores = HuggingFaceReranker._normalize_scores([3.0, 3.0, 3.0])
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assert scores == pytest.approx([_sigmoid(3.0)] * 3)
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def test_zero_logit_maps_to_half(self):
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assert HuggingFaceReranker._normalize_scores([0.0]) == pytest.approx([0.5])
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def test_empty_scores(self):
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assert HuggingFaceReranker._normalize_scores([]) == []
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