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Unit tests for :mod:`counterfeint.training.rollout`.
These exercise the per-step recorder, the action-class shaping math
inside :func:`records_to_samples`, and the side-column wiring without
spinning up an HF model or the FraudArena server.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import pytest
from counterfeint.models import AdReviewAction
from counterfeint.training.rollout import (
RecordingHFInvestigator,
TracingPolicy,
classify_action,
records_to_samples,
summarise_action,
)
# ---------------------------------------------------------------------------
# Stand-in for HFInvestigator that exposes the same recording slots.
# ---------------------------------------------------------------------------
class _FakeInvestigator:
"""Minimal stand-in matching the HFInvestigator recording contract."""
def __init__(self, plan: List[Dict[str, Any]]) -> None:
self._plan = list(plan)
self.fallback_count = 0
self.call_count = 0
self.last_prompt: Optional[str] = None
self.last_completion: Optional[str] = None
self.last_error = None
def reset(self) -> None:
self.fallback_count = 0
self.call_count = 0
self.last_prompt = None
self.last_completion = None
self.last_error = None
def act(self, _observation: Dict[str, Any]) -> AdReviewAction:
self.call_count += 1
spec = self._plan.pop(0)
# Match LLMPolicyBase.act() semantics: a fallback step leaves
# last_prompt / last_completion as None (which is what the
# recorder uses to flag the row).
self.last_prompt = None
self.last_completion = None
if spec.get("fallback"):
self.fallback_count += 1
else:
self.last_prompt = spec["prompt"]
self.last_completion = spec["completion"]
return spec["action"]
# ---------------------------------------------------------------------------
# RecordingHFInvestigator
# ---------------------------------------------------------------------------
class TestRecordingHFInvestigator:
def test_records_one_entry_per_act(self) -> None:
inner = _FakeInvestigator(
plan=[
{
"prompt": "p1", "completion": "c1",
"action": AdReviewAction(
action_type="investigate",
ad_id="ad_001",
investigation_target="payment_method",
rationale="x",
),
},
{
"prompt": "p2", "completion": "c2",
"action": AdReviewAction(
action_type="verdict",
ad_id="ad_001",
verdict="reject",
confidence=0.9,
rationale="bad payment trail",
),
},
],
)
rec = RecordingHFInvestigator(inner)
rec.reset()
rec.act({})
rec.act({})
assert len(rec.step_records) == 2
assert rec.step_records[0]["prompt"] == "p1"
assert rec.step_records[0]["completion"] == "c1"
assert rec.step_records[0]["fallback_used"] is False
assert rec.step_records[1]["completion"] == "c2"
assert rec.fallback_count == 0
def test_fallback_step_marks_record_and_skips_text(self) -> None:
inner = _FakeInvestigator(
plan=[
{
"fallback": True,
"action": AdReviewAction(
action_type="verdict",
ad_id="ad_001",
verdict="approve",
confidence=0.4,
rationale="fallback",
),
}
],
)
rec = RecordingHFInvestigator(inner)
rec.reset()
rec.act({})
assert len(rec.step_records) == 1
# _FakeInvestigator clears its slots on fallback to mimic the
# base policy's behaviour ⇒ recorder marks fallback_used.
assert rec.step_records[0]["fallback_used"] is True
assert rec.fallback_count == 1
# ---------------------------------------------------------------------------
# Reward shaping
# ---------------------------------------------------------------------------
class TestRecordsToSamples:
@staticmethod
def _record(prompt: str, completion: str, action_repr: str, step_idx: int) -> Dict[str, Any]:
return {
"step_idx": step_idx,
"prompt": prompt,
"completion": completion,
"fallback_used": False,
"action_repr": action_repr,
}
def test_mixed_actions_get_80_20_shaping_split(self) -> None:
# 1 verdict + 4 investigate steps, total reward = 1.0.
# Verdict should get 0.8 (the full 80% share, n_verdict=1).
# Each investigate step should get 0.2 / 4 = 0.05.
records = [
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 1),
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 2),
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 3),
self._record("p", "c", "AdReviewAction(action_type='verdict', ...)", 4),
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 5),
]
samples = records_to_samples(
records,
episode_result={
"grader_score": 0.5,
"rewards_by_role": {"investigator": 1.0},
"end_reason": "queue_drained",
},
task_id="task_2",
seed=42,
)
assert len(samples) == 5
verdict = next(s for s in samples if s.metadata["action_class"] == "verdict")
invests = [s for s in samples if s.metadata["action_class"] == "investigate"]
assert verdict.reward == pytest.approx(0.8, rel=1e-6)
assert len(invests) == 4
for s in invests:
assert s.reward == pytest.approx(0.05, rel=1e-6)
# Total preserves the episode reward.
assert sum(s.reward for s in samples) == pytest.approx(1.0, rel=1e-6)
# Side columns wire through correctly.
assert all(s.task_id == "task_2" for s in samples)
assert all(s.seed == 42 for s in samples)
assert verdict.terminal_grader_score == pytest.approx(0.5, rel=1e-6)
def test_uniform_split_when_only_one_action_class(self) -> None:
records = [
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 1),
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 2),
]
samples = records_to_samples(
records,
episode_result={"grader_score": 0.0, "rewards_by_role": {"investigator": 0.6}},
task_id="task_1",
seed=1,
)
assert len(samples) == 2
for s in samples:
assert s.reward == pytest.approx(0.3, rel=1e-6)
def test_fallback_only_records_are_dropped(self) -> None:
records = [
{
"step_idx": 1, "prompt": None, "completion": None,
"fallback_used": True,
"action_repr": "AdReviewAction(action_type='verdict', ...)",
},
]
samples = records_to_samples(
records,
episode_result={"rewards_by_role": {"investigator": 1.0}},
task_id="task_3",
seed=7,
)
assert samples == []
def test_link_accounts_counts_as_verdict_action_class(self) -> None:
records = [
self._record("p", "c", "AdReviewAction(action_type='link_accounts', ...)", 1),
self._record("p", "c", "AdReviewAction(action_type='investigate', ...)", 2),
]
samples = records_to_samples(
records,
episode_result={"rewards_by_role": {"investigator": 1.0}},
task_id="task_3",
seed=7,
)
link_sample = next(s for s in samples if s.step_idx == 1)
invest_sample = next(s for s in samples if s.step_idx == 2)
assert link_sample.metadata["action_class"] == "verdict"
assert invest_sample.metadata["action_class"] == "investigate"
assert link_sample.reward == pytest.approx(0.8, rel=1e-6)
assert invest_sample.reward == pytest.approx(0.2, rel=1e-6)
class TestClassifyAction:
def test_verdict_recognised(self) -> None:
assert classify_action("AdReviewAction(action_type='verdict', verdict='reject')") == "verdict"
def test_link_accounts_recognised_as_verdict(self) -> None:
assert classify_action("AdReviewAction(action_type='link_accounts', linked_ad_id='ad_002')") == "verdict"
def test_investigate_default(self) -> None:
assert classify_action("AdReviewAction(action_type='investigate', ...)") == "investigate"
def test_empty_input_default_investigate(self) -> None:
assert classify_action(None) == "investigate"
assert classify_action("") == "investigate"
# ---------------------------------------------------------------------------
# TracingPolicy + summarise_action are lightweight UX helpers; smoke test.
# ---------------------------------------------------------------------------
class TestSummariseAction:
def test_handles_action_dict(self) -> None:
out = summarise_action(
"investigator",
{"action_type": "verdict", "verdict": "reject", "confidence": 0.93,
"rationale": "payment ring"},
)
assert "verdict" in out
assert "reject" in out
assert "@0.93" in out
assert '"payment ring"' in out
def test_handles_action_object(self) -> None:
action = AdReviewAction(
action_type="link_accounts",
ad_id="ad_001",
linked_ad_id="ad_002",
link_reason="payment_id collision",
)
out = summarise_action("investigator", action)
assert "link_accounts" in out
assert "ad_002" in out
assert "payment_id collision" in out
def test_truncates_long_rationale(self) -> None:
long = "x" * 300
out = summarise_action(
"investigator",
{"action_type": "verdict", "verdict": "approve", "rationale": long},
max_rationale_chars=20,
)
assert "..." in out
# length budget includes leading/trailing quote chars.
assert len(out) < 80
class TestTracingPolicyForwarding:
def test_disabled_trace_is_silent_but_forwards(self, capsys) -> None:
inner = _FakeInvestigator(
plan=[
{
"prompt": "p", "completion": "c",
"action": AdReviewAction(
action_type="verdict",
ad_id="ad_001",
verdict="approve",
confidence=0.5,
rationale="ok",
),
}
],
)
wrapped = TracingPolicy(inner, "investigator", enabled=False)
action = wrapped.act({})
captured = capsys.readouterr()
assert captured.out == "" # silent
assert action.action_type == "verdict"
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