125 lines
4.7 KiB
Python
125 lines
4.7 KiB
Python
"""Dashboard stats — pure-helper and RQ Score client contract tests."""
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import asyncio
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import httpx
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from app.engine import stats as S
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from app.engine.stats import clients
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from app.engine.normalize import _posted_date
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from app.db.repo import _activity_score
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def test_posted_date_per_board():
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assert _posted_date({"createdDate": "2026-06-18T10:37:17Z"}) == "2026-06-18T10:37:17Z" # Naukri
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assert _posted_date({"postedDate": "2026-06-18T12:46:39Z"}) == "2026-06-18T12:46:39Z" # LinkedIn
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assert _posted_date({"date_posted": "2026-06-17T17:38:14Z"}) == "2026-06-17T17:38:14Z" # Foundit
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assert _posted_date({"jobDetails": {"createdDate": "2026-06-18T00:00:00Z"}}) is not None # nested
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assert _posted_date({"nope": "x"}) is None
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def test_match_scores_and_histogram():
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opps = [{"match": {"score": 84}}, {"matchScore": 72}, {"match": {"score": 0}}, {}]
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scores = S._match_scores(opps)
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assert sorted(scores) == [72, 84] # 0 and missing dropped
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h = S._histogram(scores)
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assert sum(h) == 2 and len(h) == 11
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def test_scout_stats_accepts_numeric_applicant_strings(monkeypatch):
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feed = {"opportunities": [{"applicants": "10"}, {"applicants": 30}, {"applicants": "unknown"}]}
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activity = {
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"matches": 3, "viewed": 0, "saved": 0, "applied": 0, "tailored": 0,
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"searches": 1, "percentile": None, "cohort_size": 1,
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}
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monkeypatch.setattr(S._repo, "get_feed", lambda _user_id: asyncio.sleep(0, result=feed))
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monkeypatch.setattr(S._repo, "get_activity_stats", lambda _user_id: asyncio.sleep(0, result=activity))
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monkeypatch.setattr(S._repo, "get_active_window", lambda _user_id: asyncio.sleep(0, result={}))
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monkeypatch.setattr(S._repo, "get_salary_band", lambda _user_id: asyncio.sleep(0, result=None))
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monkeypatch.setattr(S._clients, "fetch_qx", lambda _user_uuid: asyncio.sleep(0, result=None))
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monkeypatch.setattr(S._clients, "fetch_qx_trend", lambda _user_uuid: asyncio.sleep(0, result=None))
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monkeypatch.setattr(S._clients, "fetch_streak", lambda _user_id: asyncio.sleep(0, result=None))
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result = asyncio.run(S.build_scout_stats("user-1"))
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assert result["avgApplicants"] == 20
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def test_apply_window_freshness():
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# all None when no posting dates (honest — never fabricated)
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assert S._apply_window([{}, {"posted_date": None}])["urgency"] is None
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w = S._apply_window([{"posted_date": "2026-06-19T00:00:00Z"}, {"posted_date": "2026-06-15T00:00:00Z"}])
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assert w["dated"] == 2 and w["median_age_days"] is not None and w["urgency"] in ("high", "medium", "low")
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def test_activity_score_weighting():
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# applies weighted highest, then saves, views, searches
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assert _activity_score(0, 0, 1, 0) > _activity_score(0, 1, 0, 0) > _activity_score(1, 0, 0, 0)
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assert _activity_score(0, 0, 0, 0) == 0
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class _Response:
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status_code = 200
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def __init__(self, payload):
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self._payload = payload
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def raise_for_status(self):
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return None
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def json(self):
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return self._payload
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def test_fetch_qx_reads_strict_rq_score_contract(monkeypatch):
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async def _get(_self, _url, **_kwargs):
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return _Response({"rq_score": 68.25, "quotients": {"technical": 71}})
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monkeypatch.setattr(httpx.AsyncClient, "get", _get)
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assert asyncio.run(clients.fetch_qx("user-uuid")) == {
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"qx": 68.2,
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"quotients": {"technical": 71},
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}
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def test_fetch_qx_does_not_alias_legacy_q_score(monkeypatch):
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async def _get(_self, _url, **_kwargs):
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return _Response({"q_score": 99, "quotients": {"technical": 99}})
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monkeypatch.setattr(httpx.AsyncClient, "get", _get)
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assert asyncio.run(clients.fetch_qx("user-uuid")) is None
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def test_fetch_qx_treats_malformed_payload_as_absent(monkeypatch):
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async def _get(_self, _url, **_kwargs):
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return _Response([{"rq_score": 88}])
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monkeypatch.setattr(httpx.AsyncClient, "get", _get)
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assert asyncio.run(clients.fetch_qx("user-uuid")) is None
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def test_fetch_qx_trend_ignores_malformed_and_absent_points(monkeypatch):
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async def _get(_self, _url, **_kwargs):
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return _Response({"points": [None, {"rq_score": "bad"}, {"rq_score": 35}, {"rq_score": 40}]})
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monkeypatch.setattr(httpx.AsyncClient, "get", _get)
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assert asyncio.run(clients.fetch_qx_trend("user-uuid")) == [35.0, 40.0]
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def test_fetch_qx_trend_reads_strict_rq_score_points(monkeypatch):
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async def _get(_self, _url, **_kwargs):
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return _Response({
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"points": [
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{"rq_score": 35, "q_score": 91},
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{"rq_score": 44.16, "q_score": 92},
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{"q_score": 93},
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]
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})
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monkeypatch.setattr(httpx.AsyncClient, "get", _get)
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assert asyncio.run(clients.fetch_qx_trend("user-uuid")) == [35.0, 44.2]
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