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.
191 lines
8.9 KiB
Python
191 lines
8.9 KiB
Python
"""Composite score and red flag extraction."""
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from __future__ import annotations
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import json
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from datetime import datetime, timezone
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from typing import Optional
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from app.db.models import Seller, TrustScore
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HARD_FILTER_AGE_DAYS = 7
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HARD_FILTER_BAD_RATIO_MIN_COUNT = 20
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HARD_FILTER_BAD_RATIO_THRESHOLD = 0.80
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# Above this lifetime count the 12-month ratio may cover only a tiny recent sample,
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# making a hard bad-actor flag disproportionate. Instead we emit the softer
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# "declining_ratio" flag and let the composite score carry the penalty.
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# Note: buyer-feedback-only accounts (e.g. longtime buyers who start selling) are a
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# related edge case that requires profile-page scraping to detect properly — tracked
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# in snipe#52 as a medium-term fix.
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HARD_FILTER_BAD_RATIO_MAX_COUNT = 500
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HARD_FILTER_BAD_RATIO_HIGH_THRESHOLD = 0.60 # catastrophically bad even for high-volume
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# Sellers above this feedback count are treated as established retailers.
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# Stock photo reuse (duplicate_photo) is suppressed for them — large retailers
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# legitimately share manufacturer images across many listings.
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_ESTABLISHED_RETAILER_FEEDBACK_THRESHOLD = 1000
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# Title keywords that suggest cosmetic damage or wear (free-tier title scan).
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# Description-body scan (paid BSL feature) runs via BTF enrichment — not implemented yet.
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_SCRATCH_DENT_KEYWORDS = frozenset([
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# Explicit cosmetic damage
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"scratch", "scratched", "scratches", "scuff", "scuffed",
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"dent", "dented", "ding", "dinged",
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"crack", "cracked", "chip", "chipped",
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"damage", "damaged", "cosmetic damage",
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"blemish", "wear", "worn", "worn in",
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# Parts / condition catch-alls (also matches eBay condition field strings verbatim)
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"as is", "for parts", "parts only", "spares or repair", "parts or repair",
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"parts/repair", "parts or not working", "not working",
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# Evasive redirects — seller hiding damage detail in listing body
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"see description", "read description", "read listing", "see listing",
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"see photos for", "see pics for", "see images for",
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# Functional problem phrases (phrases > single words to avoid false positives)
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"issue with", "issues with", "problem with", "problems with",
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"not working", "stopped working", "doesn't work", "does not work",
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"no power", "dead on arrival", "powers on but", "turns on but", "boots but",
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"faulty", "broken screen", "broken hinge", "broken port",
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# DIY / project / repair listings
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"needs repair", "needs work", "needs tlc",
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"project unit", "project item", "project laptop", "project phone",
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"for repair", "sold as is",
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])
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def _has_damage_keywords(title: str) -> bool:
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lower = title.lower()
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return any(kw in lower for kw in _SCRATCH_DENT_KEYWORDS)
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_LONG_ON_MARKET_MIN_SIGHTINGS = 5
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_LONG_ON_MARKET_MIN_DAYS = 14
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_PRICE_DROP_THRESHOLD = 0.20 # 20% below first-seen price
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def _days_since(iso: Optional[str]) -> Optional[int]:
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if not iso:
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return None
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try:
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dt = datetime.fromisoformat(iso.replace("Z", "+00:00"))
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# Normalize to naive UTC so both paths (timezone-aware ISO and SQLite
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# CURRENT_TIMESTAMP naive strings) compare correctly.
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if dt.tzinfo is None:
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dt = dt.replace(tzinfo=timezone.utc)
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return (datetime.now(timezone.utc) - dt).days
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except ValueError:
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return None
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class Aggregator:
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def aggregate(
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self,
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signal_scores: dict[str, Optional[int]],
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photo_hash_duplicate: bool,
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seller: Optional[Seller],
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listing_id: int = 0,
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listing_title: str = "",
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listing_condition: str = "",
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times_seen: int = 1,
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first_seen_at: Optional[str] = None,
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price: float = 0.0,
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price_at_first_seen: Optional[float] = None,
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is_blocklisted: bool = False,
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) -> TrustScore:
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is_partial = any(v is None for v in signal_scores.values())
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clean = {k: (v if v is not None else 0) for k, v in signal_scores.items()}
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# Score only against signals that returned real data — treating "no data"
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# as 0 conflates "bad signal" with "missing signal" and drags scores down
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# unfairly when the API doesn't expose a field (e.g. registrationDate).
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available = [v for v in signal_scores.values() if v is not None]
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available_max = len(available) * 20
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if available_max > 0:
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composite = round((sum(available) / available_max) * 100)
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else:
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composite = 0
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red_flags: list[str] = []
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# Blocklist: force established_bad_actor and zero the score regardless of other signals.
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if is_blocklisted:
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red_flags.append("established_bad_actor")
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composite = 0
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return TrustScore(
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listing_id=listing_id,
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composite_score=composite,
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account_age_score=clean["account_age"],
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feedback_count_score=clean["feedback_count"],
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feedback_ratio_score=clean["feedback_ratio"],
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price_vs_market_score=clean["price_vs_market"],
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category_history_score=clean["category_history"],
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photo_hash_duplicate=photo_hash_duplicate,
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red_flags_json=json.dumps(red_flags),
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score_is_partial=is_partial,
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)
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# Hard filters
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if seller and seller.account_age_days is not None and seller.account_age_days < HARD_FILTER_AGE_DAYS:
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red_flags.append("new_account")
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if seller and seller.feedback_ratio == 0.0 and seller.feedback_count > 0:
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# 12-month ratio missing from page — returning seller or buyer-only account.
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# Score will be partial (metadata._feedback_ratio returns None). Soft flag
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# only: do NOT fire established_bad_actor on what is likely missing data.
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red_flags.append("no_recent_seller_data")
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elif seller and seller.feedback_ratio < HARD_FILTER_BAD_RATIO_THRESHOLD:
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if HARD_FILTER_BAD_RATIO_MIN_COUNT < seller.feedback_count <= HARD_FILTER_BAD_RATIO_MAX_COUNT:
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# Moderate-volume account with consistently bad ratio → hard flag.
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red_flags.append("established_bad_actor")
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elif seller.feedback_count > HARD_FILTER_BAD_RATIO_MAX_COUNT:
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if seller.feedback_ratio < HARD_FILTER_BAD_RATIO_HIGH_THRESHOLD:
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# High-volume seller with catastrophic ratio → still hard flag.
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red_flags.append("established_bad_actor")
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else:
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# High-volume seller with declining but not catastrophic ratio.
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# 12-month window may cover only a small recent sample — soft flag only.
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red_flags.append("declining_ratio")
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if seller and seller.feedback_count == 0:
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red_flags.append("zero_feedback")
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# Zero feedback is a deliberate signal, not missing data — cap composite score
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# so a 0-feedback seller can never appear trustworthy on other signals alone.
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composite = min(composite, 35)
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# Soft flags
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if seller and seller.account_age_days is not None and seller.account_age_days < 30:
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red_flags.append("account_under_30_days")
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if seller and seller.feedback_count < 10:
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red_flags.append("low_feedback_count")
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if signal_scores.get("price_vs_market") == 0: # only flag when data exists and price is genuinely <50% of market
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red_flags.append("suspicious_price")
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is_established_retailer = (
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seller is not None
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and seller.feedback_count >= _ESTABLISHED_RETAILER_FEEDBACK_THRESHOLD
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)
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if photo_hash_duplicate and not is_established_retailer:
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red_flags.append("duplicate_photo")
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if (listing_title and _has_damage_keywords(listing_title)) or (
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listing_condition and _has_damage_keywords(listing_condition)
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):
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red_flags.append("scratch_dent_mentioned")
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# Staging DB signals
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days_in_index = _days_since(first_seen_at)
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if (times_seen >= _LONG_ON_MARKET_MIN_SIGHTINGS
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and days_in_index is not None
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and days_in_index >= _LONG_ON_MARKET_MIN_DAYS):
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red_flags.append("long_on_market")
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if (price_at_first_seen and price_at_first_seen > 0
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and price < price_at_first_seen * (1 - _PRICE_DROP_THRESHOLD)):
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red_flags.append("significant_price_drop")
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return TrustScore(
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listing_id=listing_id,
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composite_score=composite,
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account_age_score=clean["account_age"],
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feedback_count_score=clean["feedback_count"],
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feedback_ratio_score=clean["feedback_ratio"],
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price_vs_market_score=clean["price_vs_market"],
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category_history_score=clean["category_history"],
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photo_hash_duplicate=photo_hash_duplicate,
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red_flags_json=json.dumps(red_flags),
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score_is_partial=is_partial,
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)
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