pyr0ball
bccedb1fe5
eBay omits the 12-month positive percentage for returning sellers and buyer-only accounts with no recent sales. Previously ratio=0.0 with count>0 triggered established_bad_actor; now it returns None from the scorer (score_is_partial=True) and emits a soft no_recent_seller_data flag instead. ratio=0.0 with count=0 is still treated as no-history.
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
from app.db.models import Seller
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from app.trust.metadata import MetadataScorer
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def _seller(**kwargs) -> Seller:
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defaults = dict(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=730, feedback_count=450,
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feedback_ratio=0.991, category_history_json='{"ELECTRONICS": 30}',
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)
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defaults.update(kwargs)
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return Seller(**defaults)
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def test_established_seller_scores_high():
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scorer = MetadataScorer()
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scores = scorer.score(_seller(), market_median=1000.0, listing_price=950.0)
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total = sum(scores.values())
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assert total >= 80
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def test_new_account_scores_zero_on_age():
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scorer = MetadataScorer()
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scores = scorer.score(_seller(account_age_days=3), market_median=1000.0, listing_price=950.0)
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assert scores["account_age"] == 0
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def test_low_feedback_count_scores_low():
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scorer = MetadataScorer()
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scores = scorer.score(_seller(feedback_count=2), market_median=1000.0, listing_price=950.0)
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assert scores["feedback_count"] < 10
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def test_suspicious_price_scores_zero():
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scorer = MetadataScorer()
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# 60% below market → zero
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scores = scorer.score(_seller(), market_median=1000.0, listing_price=400.0)
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assert scores["price_vs_market"] == 0
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def test_no_market_data_returns_none():
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scorer = MetadataScorer()
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scores = scorer.score(_seller(), market_median=None, listing_price=950.0)
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# None signals "data unavailable" — aggregator will set score_is_partial=True
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assert scores["price_vs_market"] is None
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def test_zero_ratio_with_nonzero_count_returns_none():
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"""ratio=0.0 with count>0 means eBay didn't show a 12-month percentage.
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Must return None (missing data) not 0 (catastrophically bad)."""
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scorer = MetadataScorer()
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scores = scorer.score(
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_seller(feedback_ratio=0.0, feedback_count=117),
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market_median=None, listing_price=500.0,
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)
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assert scores["feedback_ratio"] is None
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def test_zero_ratio_with_zero_count_scores_low():
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"""feedback_ratio=0.0 with count=0 is a real 'no data at all' case, not missing."""
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scorer = MetadataScorer()
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scores = scorer.score(
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_seller(feedback_ratio=0.0, feedback_count=0),
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market_median=None, listing_price=500.0,
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)
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# count=0 means zero_feedback; ratio=0 with count=0 is the standard no-history path
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# (not the "missing 12-month window" path)
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assert scores["feedback_ratio"] == 5 # ratio < 0.90 → 5
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