pyr0ball
ea78b9c2cd
- Parallel execution: search() and get_completed_sales() now run
concurrently via ThreadPoolExecutor — each gets its own Store/SQLite
connection for thread safety. First cold search time ~halved.
- Pagination: SearchFilters.pages (default 1) controls how many eBay
result pages are fetched. Both search and sold-comps support up to 3
parallel Playwright sessions per call (capped to avoid Xvfb overload).
UI: segmented 1/2/3/5 pages selector in filter sidebar with cost hint.
- True median: get_completed_sales() now averages the two middle values
for even-length price lists instead of always taking the lower bound.
- Fix suspicious_price false positive: aggregator now checks
signal_scores.get("price_vs_market") == 0 (pre-None-substitution)
so listings without market data are never flagged as suspicious.
- Fix title pollution: scraper strips eBay's hidden screen-reader span
("Opens in a new window or tab") from listing titles via regex.
Lazy-imports playwright/playwright_stealth inside _get() so pure
parsing functions are importable without the full browser stack.
- Tests: 48 pass on host (scraper tests now runnable without Docker),
new regression guards for all three bug fixes.
95 lines
3.9 KiB
Python
95 lines
3.9 KiB
Python
from app.db.models import Seller
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from app.trust.aggregator import Aggregator
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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_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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