pyr0ball
05f845962f
Fixes a false-positive edge case (snipe#52) where sellers with 500+ lifetime feedback were hard-flagged as established_bad_actor when the 12-month ratio dipped below 80% — even though the 12-month window may cover only a small recent sample relative to lifetime history. Changes: - established_bad_actor hard filter now only fires for accounts with 20–500 lifetime feedback (unchanged behavior for moderate accounts) - Accounts >500 feedback with ratio 60–80%: new declining_ratio soft flag (composite score penalised but not zeroed, no hard block) - Accounts >500 feedback with ratio <60%: still established_bad_actor (catastrophically bad even for high-volume sellers) - Two new constants: HARD_FILTER_BAD_RATIO_MAX_COUNT=500, HARD_FILTER_BAD_RATIO_HIGH_THRESHOLD=0.60 Note: buyer-feedback-only accounts (lifetime buyer history inflating feedback_count for new sellers) requires profile-page scraping to detect properly — tracked in snipe#52 as medium-term work. Tests: 22 passed
298 lines
13 KiB
Python
298 lines
13 KiB
Python
from datetime import datetime, timedelta, timezone
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from app.db.models import Seller
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from app.trust.aggregator import Aggregator
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_ALL_20 = {k: 20 for k in ["account_age", "feedback_count", "feedback_ratio", "price_vs_market", "category_history"]}
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def _iso_days_ago(n: int) -> str:
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return (datetime.now(timezone.utc) - timedelta(days=n)).isoformat()
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def test_composite_sum_of_five_signals():
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agg = Aggregator()
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scores = {
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"account_age": 18, "feedback_count": 16,
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"feedback_ratio": 20, "price_vs_market": 15,
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"category_history": 14,
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}
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=None)
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assert result.composite_score == 83
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def test_hard_filter_new_account():
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agg = Aggregator()
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scores = {k: 20 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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young_seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=3, feedback_count=0,
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feedback_ratio=1.0, category_history_json="{}",
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)
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=young_seller)
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assert "new_account" in result.red_flags_json
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def test_hard_filter_bad_actor_established_account():
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"""Established account (count > 20) with very bad ratio → hard filter."""
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agg = Aggregator()
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scores = {k: 10 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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bad_seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=730, feedback_count=25, # count > 20
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feedback_ratio=0.70, # ratio < 80% → hard filter
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category_history_json="{}",
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)
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=bad_seller)
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assert "established_bad_actor" in result.red_flags_json
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def test_partial_score_flagged_when_signals_missing():
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agg = Aggregator()
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scores = {
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"account_age": 18, "feedback_count": None, # None = unavailable
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"feedback_ratio": 20, "price_vs_market": 15,
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"category_history": 14,
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}
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=None)
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assert result.score_is_partial is True
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def test_suspicious_price_not_flagged_when_market_data_absent():
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"""None price_vs_market (no market comp) must NOT trigger suspicious_price.
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Regression guard: clean[] replaces None with 0, so naive `clean[...] == 0`
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would fire even when the signal is simply unavailable.
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"""
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agg = Aggregator()
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scores = {
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"account_age": 15, "feedback_count": 15,
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"feedback_ratio": 20, "price_vs_market": None, # no market data
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"category_history": 0,
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}
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=None)
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assert "suspicious_price" not in result.red_flags_json
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def test_suspicious_price_flagged_when_price_genuinely_low():
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"""price_vs_market == 0 (explicitly, meaning >50% below median) → flag fires."""
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agg = Aggregator()
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scores = {
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"account_age": 15, "feedback_count": 15,
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"feedback_ratio": 20, "price_vs_market": 0, # price is scam-level low
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"category_history": 0,
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}
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=None)
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assert "suspicious_price" in result.red_flags_json
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def test_scratch_dent_flagged_from_title_slash_variant():
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"""Title containing 'parts/repair' (slash variant, no 'or') must trigger scratch_dent_mentioned."""
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agg = Aggregator()
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scores = {k: 15 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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result = agg.aggregate(
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scores, photo_hash_duplicate=False, seller=None,
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listing_title="Generic Widget XL - Parts/Repair",
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)
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assert "scratch_dent_mentioned" in result.red_flags_json
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def test_scratch_dent_flagged_from_condition_field():
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"""eBay formal condition 'for parts or not working' must trigger scratch_dent_mentioned
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even when the listing title contains no damage keywords."""
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agg = Aggregator()
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scores = {k: 15 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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result = agg.aggregate(
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scores, photo_hash_duplicate=False, seller=None,
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listing_title="Generic Widget XL",
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listing_condition="for parts or not working",
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)
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assert "scratch_dent_mentioned" in result.red_flags_json
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def test_scratch_dent_not_flagged_for_clean_listing():
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"""Clean title + 'New' condition must NOT trigger scratch_dent_mentioned."""
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agg = Aggregator()
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scores = {k: 15 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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result = agg.aggregate(
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scores, photo_hash_duplicate=False, seller=None,
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listing_title="Generic Widget XL",
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listing_condition="new",
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)
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assert "scratch_dent_mentioned" not in result.red_flags_json
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def test_new_account_not_flagged_when_age_absent():
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"""account_age_days=None (scraper tier) must NOT trigger new_account or account_under_30_days."""
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agg = Aggregator()
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scores = {k: 10 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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scraper_seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=None, # not fetched at scraper tier
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feedback_count=50, feedback_ratio=0.99, category_history_json="{}",
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)
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=scraper_seller)
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assert "new_account" not in result.red_flags_json
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assert "account_under_30_days" not in result.red_flags_json
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# ── zero_feedback ─────────────────────────────────────────────────────────────
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def test_zero_feedback_adds_flag():
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"""seller.feedback_count == 0 must add zero_feedback flag."""
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agg = Aggregator()
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seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=365, feedback_count=0, feedback_ratio=1.0,
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category_history_json="{}",
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)
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result = agg.aggregate(_ALL_20.copy(), photo_hash_duplicate=False, seller=seller)
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assert "zero_feedback" in result.red_flags_json
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def test_zero_feedback_caps_composite_at_35():
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"""Even with perfect other signals (all 20/20), zero feedback caps composite at 35."""
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agg = Aggregator()
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seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=365, feedback_count=0, feedback_ratio=1.0,
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category_history_json="{}",
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)
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result = agg.aggregate(_ALL_20.copy(), photo_hash_duplicate=False, seller=seller)
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assert result.composite_score <= 35
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# ── long_on_market ────────────────────────────────────────────────────────────
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def test_long_on_market_flagged_when_thresholds_met():
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"""times_seen >= 5 AND listing age >= 14 days → long_on_market fires."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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times_seen=5, first_seen_at=_iso_days_ago(20),
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)
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assert "long_on_market" in result.red_flags_json
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def test_long_on_market_not_flagged_when_too_few_sightings():
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"""times_seen < 5 must NOT trigger long_on_market even if listing is old."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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times_seen=4, first_seen_at=_iso_days_ago(30),
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)
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assert "long_on_market" not in result.red_flags_json
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def test_long_on_market_not_flagged_when_too_recent():
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"""times_seen >= 5 but only seen for < 14 days → long_on_market must NOT fire."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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times_seen=10, first_seen_at=_iso_days_ago(5),
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)
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assert "long_on_market" not in result.red_flags_json
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# ── significant_price_drop ────────────────────────────────────────────────────
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def test_significant_price_drop_flagged():
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"""price >= 20% below price_at_first_seen → significant_price_drop fires."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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price=75.00, price_at_first_seen=100.00, # 25% drop
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)
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assert "significant_price_drop" in result.red_flags_json
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def test_significant_price_drop_not_flagged_when_drop_is_small():
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"""< 20% drop must NOT trigger significant_price_drop."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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price=95.00, price_at_first_seen=100.00, # 5% drop
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)
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assert "significant_price_drop" not in result.red_flags_json
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def test_significant_price_drop_not_flagged_when_no_prior_price():
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"""price_at_first_seen=None (first sighting) must NOT fire significant_price_drop."""
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agg = Aggregator()
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result = agg.aggregate(
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_ALL_20.copy(), photo_hash_duplicate=False, seller=None,
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price=50.00, price_at_first_seen=None,
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)
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assert "significant_price_drop" not in result.red_flags_json
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# ── declining_ratio (high-volume seller edge case, snipe#52) ─────────────────
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def test_declining_ratio_soft_flag_for_high_volume_seller():
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"""High-volume seller (count > 500) with declining but not catastrophic ratio
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gets declining_ratio soft flag, NOT the hard established_bad_actor flag.
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Edge case: 12-month ratio may reflect only a small recent sample for sellers
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with large lifetime feedback counts — hard-flagging is disproportionate.
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"""
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agg = Aggregator()
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scores = {k: 10 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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high_vol = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=2000, feedback_count=800, # count > 500
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feedback_ratio=0.75, # < 0.80 but > 0.60
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category_history_json="{}",
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)
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=high_vol)
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assert "declining_ratio" in result.red_flags_json
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assert "established_bad_actor" not in result.red_flags_json
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def test_established_bad_actor_still_fires_for_catastrophic_high_volume_ratio():
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"""High-volume seller (count > 500) with catastrophically bad ratio (< 60%)
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still gets the hard established_bad_actor flag — not just declining_ratio."""
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agg = Aggregator()
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scores = {k: 10 for k in ["account_age", "feedback_count",
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"feedback_ratio", "price_vs_market", "category_history"]}
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bad_high_vol = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=2000, feedback_count=800,
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feedback_ratio=0.50, # < 0.60 threshold → still hard flag
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category_history_json="{}",
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)
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result = agg.aggregate(scores, photo_hash_duplicate=False, seller=bad_high_vol)
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assert "established_bad_actor" in result.red_flags_json
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assert "declining_ratio" not in result.red_flags_json
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# ── established retailer ──────────────────────────────────────────────────────
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def test_established_retailer_suppresses_duplicate_photo():
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"""feedback_count >= 1000 (established retailer) must suppress duplicate_photo flag."""
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agg = Aggregator()
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retailer = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=1800, feedback_count=5000, feedback_ratio=0.99,
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category_history_json="{}",
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)
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result = agg.aggregate(_ALL_20.copy(), photo_hash_duplicate=True, seller=retailer)
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assert "duplicate_photo" not in result.red_flags_json
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def test_non_retailer_does_not_suppress_duplicate_photo():
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"""feedback_count < 1000 — duplicate_photo must still fire when hash matches."""
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agg = Aggregator()
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seller = Seller(
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platform="ebay", platform_seller_id="u", username="u",
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account_age_days=365, feedback_count=50, feedback_ratio=0.99,
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category_history_json="{}",
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)
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result = agg.aggregate(_ALL_20.copy(), photo_hash_duplicate=True, seller=seller)
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assert "duplicate_photo" in result.red_flags_json
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