120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
"""Operator-tunable planner knobs (apply_payload / current_payload).
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§3c of the realism handoff: the planner reads mutable module globals
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that an admin can override via PUT /api/v1/realism/config. These tests
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pin the validation surface and the payload roundtrip so a regression
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that breaks operator tunables surfaces here, not on a live fleet.
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"""
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from __future__ import annotations
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import pytest
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from decnet.realism import planner
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from decnet.realism.taxonomy import ContentClass
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@pytest.fixture(autouse=True)
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def _reset_after_each_test():
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yield
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planner.reset_to_defaults()
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def test_current_payload_returns_defaults_after_reset():
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planner.reset_to_defaults()
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payload = planner.current_payload()
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assert payload["canary_probability"] == pytest.approx(0.03)
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user = {e["content_class"]: e["weight"] for e in payload["user_class_weights"]}
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assert user[ContentClass.NOTE.value] == 30
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assert user[ContentClass.TODO.value] == 20
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def test_apply_payload_overrides_user_weights():
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planner.apply_payload({
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"user_class_weights": [
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{"content_class": "note", "weight": 5},
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{"content_class": "todo", "weight": 95},
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],
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})
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payload = planner.current_payload()
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weights = {e["content_class"]: e["weight"] for e in payload["user_class_weights"]}
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assert weights == {"note": 5, "todo": 95}
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# System weights left untouched by a partial body.
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assert payload["system_class_weights"]
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def test_apply_payload_overrides_canary_probability():
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planner.apply_payload({"canary_probability": 0.15})
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assert planner.current_payload()["canary_probability"] == pytest.approx(0.15)
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def test_apply_payload_rejects_bad_canary_probability():
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with pytest.raises(ValueError, match="canary_probability"):
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planner.apply_payload({"canary_probability": 1.5})
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with pytest.raises(ValueError, match="canary_probability"):
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planner.apply_payload({"canary_probability": -0.1})
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with pytest.raises(ValueError, match="canary_probability"):
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planner.apply_payload({"canary_probability": "high"})
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def test_apply_payload_rejects_negative_weight():
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with pytest.raises(ValueError, match="non-negative integer"):
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planner.apply_payload({
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"user_class_weights": [{"content_class": "note", "weight": -1}],
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})
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def test_apply_payload_rejects_unknown_content_class():
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with pytest.raises(ValueError, match="unknown content_class"):
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planner.apply_payload({
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"user_class_weights": [{"content_class": "vibes", "weight": 1}],
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})
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def test_apply_payload_drops_class_from_wrong_list():
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"""A canary class on the user list is silently dropped (operator
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error), not raised — the partial save still applies the legit
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entries. Roundtrip shows the operator their entry didn't land."""
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planner.apply_payload({
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"user_class_weights": [
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{"content_class": "note", "weight": 10},
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{"content_class": "canary_aws_creds", "weight": 100},
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],
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})
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weights = {
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e["content_class"]: e["weight"]
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for e in planner.current_payload()["user_class_weights"]
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}
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assert weights == {"note": 10}
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# canary class did NOT bleed onto the user list.
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assert "canary_aws_creds" not in weights
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def test_apply_payload_rejects_zero_total_weight():
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with pytest.raises(ValueError, match="positive number"):
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planner.apply_payload({
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"user_class_weights": [{"content_class": "note", "weight": 0}],
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})
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def test_apply_payload_partial_failure_leaves_state_intact():
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"""If validation rejects part of a payload, the planner's other
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fields must not have been silently rebound."""
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planner.apply_payload({"canary_probability": 0.10})
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pre = planner.current_payload()
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with pytest.raises(ValueError):
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planner.apply_payload({
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"user_class_weights": [{"content_class": "note", "weight": 5}],
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"canary_probability": 9.0, # invalid
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})
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post = planner.current_payload()
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assert post == pre # nothing rebound on failure
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def test_apply_payload_ignores_unknown_keys():
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"""Forward-compat: future fields land without breaking older clients."""
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planner.apply_payload({"future_knob": "ignored"})
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# Nothing changed.
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assert planner.current_payload()["canary_probability"] == pytest.approx(0.03)
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