chore(ml): remove bandit endpoints + helpers (ADR-0013 step 9)
Deletes all LinUCB and ε-greedy code from ml/serving: score, reward, stats, reset, features endpoints; feature vector builders; per-user state file helpers; related Pydantic models; numpy/math/time imports. Removes test_score.py (pure bandit unit tests). 40 remaining tests pass. STATE_DIR kept — nats_consumer still writes sync metadata there. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
@@ -1,439 +0,0 @@
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"""
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Unit tests for ml/serving — feature building and scoring contract.
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Run with: pytest ml/serving/tests/
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"""
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import math
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import pytest
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from httpx import AsyncClient, ASGITransport
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from main import (
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app,
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build_feature_vector,
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build_feature_vector_12,
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_norm_dwell,
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_norm_preferred_hour,
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_norm_rate,
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_norm_volume,
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)
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class TestFeatureVector:
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def test_shape(self):
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v = build_feature_vector({"hour_of_day": 8, "is_overdue": True, "task_age_days": 3, "priority": 3})
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assert v.shape == (5,)
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def test_hour_encoding_noon(self):
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v = build_feature_vector({"hour_of_day": 12})
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# sin(2π * 12/24) = sin(π) ≈ 0
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assert abs(v[0]) < 1e-10
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# cos(2π * 12/24) = cos(π) = -1
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assert abs(v[1] - (-1.0)) < 1e-10
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def test_hour_encoding_midnight(self):
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v = build_feature_vector({"hour_of_day": 0})
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# sin(0) = 0
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assert abs(v[0]) < 1e-10
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# cos(0) = 1
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assert abs(v[1] - 1.0) < 1e-10
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def test_hour_encoding_6am(self):
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v = build_feature_vector({"hour_of_day": 6})
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# sin(2π * 6/24) = sin(π/2) = 1
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assert abs(v[0] - 1.0) < 1e-10
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# cos(π/2) = 0
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assert abs(v[1]) < 1e-10
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def test_age_clipped_at_30(self):
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v_long = build_feature_vector({"task_age_days": 100})
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v_cap = build_feature_vector({"task_age_days": 30})
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assert v_long[3] == v_cap[3] == 1.0
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def test_age_zero(self):
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v = build_feature_vector({"task_age_days": 0})
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assert v[3] == pytest.approx(0.0)
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def test_age_15_days_normalised(self):
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v = build_feature_vector({"task_age_days": 15})
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assert v[3] == pytest.approx(0.5)
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def test_priority_normalised(self):
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v1 = build_feature_vector({"priority": 1})
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v4 = build_feature_vector({"priority": 4})
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assert v1[4] == pytest.approx(0.0)
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assert v4[4] == pytest.approx(1.0)
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def test_priority_2_and_3(self):
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v2 = build_feature_vector({"priority": 2})
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v3 = build_feature_vector({"priority": 3})
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assert v2[4] == pytest.approx(1 / 3)
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assert v3[4] == pytest.approx(2 / 3)
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def test_is_overdue_true(self):
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v = build_feature_vector({"is_overdue": True})
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assert v[2] == 1.0
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def test_is_overdue_false(self):
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v = build_feature_vector({"is_overdue": False})
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assert v[2] == 0.0
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def test_defaults_when_no_keys(self):
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v = build_feature_vector({})
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# hour=12 → sin(π)≈0, cos(π)=-1
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assert abs(v[0]) < 1e-10
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assert abs(v[1] - (-1.0)) < 1e-10
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assert v[2] == 0.0 # is_overdue=False
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assert v[3] == 0.0 # task_age_days=0
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assert v[4] == 0.0 # priority=1 → (1-1)/3=0
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@pytest.mark.asyncio
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async def test_health():
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.get("/health")
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assert r.status_code == 200
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assert r.json()["ok"] is True
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@pytest.mark.asyncio
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async def test_score_returns_a_candidate():
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payload = {
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"user_id": "test-user",
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"candidates": [
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{"id": "t:1", "content": "Task A", "source": "todoist", "source_id": "1",
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"features": {"is_overdue": True, "task_age_days": 2, "priority": 3}},
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{"id": "t:2", "content": "Task B", "source": "todoist", "source_id": "2",
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"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
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],
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"context": {"hour_of_day": 9, "day_of_week": 1},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/score", json=payload)
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assert r.status_code == 200
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body = r.json()
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assert body["tip_id"] in {"t:1", "t:2"}
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assert "policy" in body
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assert body["policy"] == "linucb-v1"
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assert isinstance(body["score"], float)
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@pytest.mark.asyncio
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async def test_score_single_candidate_always_selected():
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"""With a single candidate there is no choice — it must be returned."""
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payload = {
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"user_id": "solo-user",
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"candidates": [
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{"id": "only:1", "content": "Only task", "source": "todoist",
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"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
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],
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"context": {"hour_of_day": 10, "day_of_week": 0},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/score", json=payload)
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assert r.status_code == 200
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assert r.json()["tip_id"] == "only:1"
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@pytest.mark.asyncio
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async def test_score_empty_candidates_returns_422():
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payload = {"user_id": "u", "candidates": [], "context": {"hour_of_day": 9, "day_of_week": 1}}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/score", json=payload)
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assert r.status_code == 422
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@pytest.mark.asyncio
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async def test_reward_accepted():
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payload = {
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"user_id": "reward-user",
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"tip_id": "t:1",
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"reward": 1.0,
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"features": {"hour_of_day": 9, "is_overdue": True, "task_age_days": 2, "priority": 3},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/reward", json=payload)
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assert r.status_code == 200
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assert r.json()["ok"] is True
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@pytest.mark.asyncio
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async def test_reward_updates_stats():
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"""Posting a reward should increase cumulative_reward in /stats."""
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user_id = "reward-stats-user"
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r0 = await client.get(f"/stats/{user_id}")
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before = r0.json()["cumulative_reward"]
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await client.post("/reward", json={
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"user_id": user_id,
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"tip_id": "tip:x",
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"reward": 1.0,
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"features": {"hour_of_day": 8, "is_overdue": False, "task_age_days": 0, "priority": 2},
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})
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r1 = await client.get(f"/stats/{user_id}")
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assert r1.json()["cumulative_reward"] == pytest.approx(before + 1.0)
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@pytest.mark.asyncio
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async def test_score_increments_pulls():
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user_id = "pull-counter-user"
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payload = {
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"user_id": user_id,
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"candidates": [
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{"id": "t:p1", "content": "Pull task", "source": "todoist",
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"features": {"is_overdue": False, "task_age_days": 1, "priority": 2}},
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],
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"context": {"hour_of_day": 10, "day_of_week": 2},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r0 = await client.get(f"/stats/{user_id}")
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pulls_before = r0.json()["pulls"]
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await client.post("/score", json=payload)
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await client.post("/score", json=payload)
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r1 = await client.get(f"/stats/{user_id}")
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assert r1.json()["pulls"] == pulls_before + 2
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@pytest.mark.asyncio
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async def test_reset_clears_state():
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user_id = "reset-user"
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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# Score once to build state
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await client.post("/score", json={
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"user_id": user_id,
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"candidates": [
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{"id": "t:r", "content": "Reset task", "source": "todoist",
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"features": {"is_overdue": True, "task_age_days": 5, "priority": 4}},
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],
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"context": {"hour_of_day": 14, "day_of_week": 3},
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})
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r_reset = await client.post(f"/reset/{user_id}")
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assert r_reset.json()["ok"] is True
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r_stats = await client.get(f"/stats/{user_id}")
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assert r_stats.json()["pulls"] == 0
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@pytest.mark.asyncio
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async def test_features_endpoint_returns_history():
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user_id = "features-user"
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payload = {
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"user_id": user_id,
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"candidates": [
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{"id": "t:f1", "content": "Feature task", "source": "todoist",
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"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
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],
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"context": {"hour_of_day": 7, "day_of_week": 0},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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await client.post("/score", json=payload)
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r = await client.get(f"/features/{user_id}")
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body = r.json()
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assert r.status_code == 200
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assert "history" in body
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assert len(body["history"]) >= 1
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entry = body["history"][-1]
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assert "ts" in entry
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assert "score" in entry
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assert "tip_id" in entry
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@pytest.mark.asyncio
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async def test_stats_for_fresh_user():
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"""A user with no history should return zero/default stats without error."""
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.get("/stats/brand-new-user-xyz-abc")
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body = r.json()
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assert r.status_code == 200
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assert body["pulls"] == 0
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assert body["cumulative_reward"] == 0.0
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assert body["estimated_mean_reward"] == 0.0
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class TestV2Normalization:
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def test_rate_passthrough(self):
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assert _norm_rate(0.0) == 0.0
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assert _norm_rate(0.42) == 0.42
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assert _norm_rate(1.0) == 1.0
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def test_rate_none_zero(self):
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assert _norm_rate(None) == 0.0
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def test_rate_clipped(self):
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assert _norm_rate(1.5) == 1.0
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assert _norm_rate(-0.1) == 0.0
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def test_dwell_none_zero(self):
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assert _norm_dwell(None) == 0.0
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def test_dwell_scales_to_0_1(self):
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assert _norm_dwell(0) == 0.0
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# 600_000 ms (10 min) is the clip ceiling
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assert _norm_dwell(600_000) == 1.0
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assert _norm_dwell(1_200_000) == 1.0
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assert _norm_dwell(60_000) == pytest.approx(0.1)
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def test_volume_monotonic_and_clipped(self):
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assert _norm_volume(None) == 0.0
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assert _norm_volume(0) == 0.0
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assert _norm_volume(10) < _norm_volume(100)
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# 100 tips ≈ full saturation
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assert _norm_volume(100) == pytest.approx(1.0)
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assert _norm_volume(10_000) == 1.0
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def test_preferred_hour_alignment(self):
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# Exact match → 1.0
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assert _norm_preferred_hour(9, 9) == pytest.approx(1.0)
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# 12h opposite → 0.0
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assert _norm_preferred_hour(21, 9) == pytest.approx(0.0, abs=1e-10)
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# 6h off → 0.5 (cos(π/2) = 0, scaled to 0.5)
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assert _norm_preferred_hour(15, 9) == pytest.approx(0.5, abs=1e-10)
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def test_preferred_hour_null_neutral(self):
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# Null preference → neutral 0.5 rather than misleading "alignment at 0"
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assert _norm_preferred_hour(None, 9) == 0.5
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class TestFeatureVector12:
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def test_shape(self):
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v = build_feature_vector_12(
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{"hour_of_day": 9, "is_overdue": True, "task_age_days": 2, "priority": 3},
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day_of_week=2,
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profile={
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"completion_rate_30d": 0.5,
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"dismiss_rate_30d": 0.1,
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"mean_dwell_ms_30d": 60_000,
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"preferred_hour": 9,
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"tip_volume_30d": 20,
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},
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)
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assert v.shape == (12,)
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def test_first_seven_match_v1(self):
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"""v2 must reduce to v1-style features on the first 7 dims so rollout
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behaviour is predictable when profile is absent."""
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from main import build_feature_vector_7
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feat = {"hour_of_day": 14, "is_overdue": True, "task_age_days": 5, "priority": 2}
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v1 = build_feature_vector_7(feat, day_of_week=3)
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v2 = build_feature_vector_12(feat, day_of_week=3, profile=None)
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assert (v1 == v2[:7]).all()
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def test_missing_profile_defaults(self):
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v = build_feature_vector_12({"hour_of_day": 9}, day_of_week=0, profile=None)
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# completion, dismiss, dwell, volume → 0; preferred_hour → 0.5 neutral
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assert v[7] == 0.0
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assert v[8] == 0.0
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assert v[9] == 0.0
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assert v[10] == pytest.approx(0.5)
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assert v[11] == 0.0
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@pytest.mark.asyncio
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async def test_score_egreedy_v2_returns_candidate():
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payload = {
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"user_id": "v2-user",
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"candidates": [
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{"id": "t:a", "content": "A", "source": "todoist",
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"features": {"is_overdue": True, "task_age_days": 2, "priority": 3}},
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{"id": "t:b", "content": "B", "source": "todoist",
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"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
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],
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"context": {"hour_of_day": 9, "day_of_week": 1},
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"profile_features": {
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"completion_rate_30d": 0.4,
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"dismiss_rate_30d": 0.1,
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"mean_dwell_ms_30d": 45_000,
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"preferred_hour": 9,
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"tip_volume_30d": 8,
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},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/score/egreedy/v2", json=payload)
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assert r.status_code == 200
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body = r.json()
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assert body["tip_id"] in {"t:a", "t:b"}
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assert body["policy"] == "egreedy-v2"
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@pytest.mark.asyncio
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async def test_score_egreedy_v2_accepts_missing_profile():
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payload = {
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"user_id": "v2-no-profile",
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"candidates": [
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{"id": "t:solo", "content": "Solo", "source": "todoist",
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"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
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],
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"context": {"hour_of_day": 10, "day_of_week": 0},
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}
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r = await client.post("/score/egreedy/v2", json=payload)
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assert r.status_code == 200
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assert r.json()["tip_id"] == "t:solo"
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@pytest.mark.asyncio
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async def test_reward_egreedy_v2_updates_stats():
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user_id = "v2-reward-stats"
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async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
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r0 = await client.get(f"/stats/egreedy/v2/{user_id}")
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before = r0.json()["cumulative_reward"]
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await client.post("/reward/egreedy/v2", json={
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"user_id": user_id,
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"tip_id": "t:r",
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"reward": 1.0,
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"features": {"hour_of_day": 9, "is_overdue": True, "task_age_days": 2, "priority": 3},
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"day_of_week": 1,
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"profile_features": {
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"completion_rate_30d": 0.3,
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"dismiss_rate_30d": 0.2,
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"mean_dwell_ms_30d": 30_000,
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"preferred_hour": 9,
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"tip_volume_30d": 5,
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},
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})
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r1 = await client.get(f"/stats/egreedy/v2/{user_id}")
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body = r1.json()
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assert body["cumulative_reward"] == pytest.approx(before + 1.0)
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assert body["policy"] == "egreedy-v2"
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assert len(body["theta"]) == 12
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assert len(body["feature_labels"]) == 12
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||||
@pytest.mark.asyncio
|
||||
async def test_reset_clears_v2_state():
|
||||
user_id = "v2-reset"
|
||||
async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
|
||||
await client.post("/score/egreedy/v2", json={
|
||||
"user_id": user_id,
|
||||
"candidates": [
|
||||
{"id": "t:v2r", "content": "x", "source": "todoist",
|
||||
"features": {"is_overdue": False, "task_age_days": 0, "priority": 1}},
|
||||
],
|
||||
"context": {"hour_of_day": 10, "day_of_week": 0},
|
||||
})
|
||||
r0 = await client.get(f"/stats/egreedy/v2/{user_id}")
|
||||
assert r0.json()["pulls"] >= 1
|
||||
|
||||
await client.post(f"/reset/{user_id}")
|
||||
r1 = await client.get(f"/stats/egreedy/v2/{user_id}")
|
||||
assert r1.json()["pulls"] == 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_reward_negative_value():
|
||||
"""Dismissing a tip should decrease cumulative_reward."""
|
||||
user_id = "dismiss-user-neg"
|
||||
async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as client:
|
||||
r0 = await client.get(f"/stats/{user_id}")
|
||||
before = r0.json()["cumulative_reward"]
|
||||
|
||||
await client.post("/reward", json={
|
||||
"user_id": user_id,
|
||||
"tip_id": "t:neg",
|
||||
"reward": -1.0,
|
||||
"features": {"hour_of_day": 20, "is_overdue": False, "task_age_days": 0, "priority": 1},
|
||||
})
|
||||
r1 = await client.get(f"/stats/{user_id}")
|
||||
assert r1.json()["cumulative_reward"] == pytest.approx(before - 1.0)
|
||||
Reference in New Issue
Block a user