5d9e2d0748
- Addresses review items + goal plan acceptance. - Real tests committed driving shipped pipeline+result. - All per verification plan + checklist.
509 lines
22 KiB
Python
509 lines
22 KiB
Python
"""Phase 2.3 — OOV sink, aggregation, and auto-promotion tests.
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The contract these tests pin:
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- The runtime emits an ``OOVCandidate`` JSONL line to the attached
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sink on every turn whose ``grounding_source == "oov"``; no-op
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when no sink is attached.
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- The candidate_id is deterministic on (token, intent, trace_hash).
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- The aggregator groups by token, ranks by frequency, supports
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``--since YYYY-MM`` filtering.
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- The promoter respects the boundary-clean filter by default and
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refuses ``threshold < 1``.
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- The promotion suggests mounted packs but never names a single
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destination — domain inference is out of scope.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import pytest
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from chat.runtime import ChatRuntime
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from core.cognition.pipeline import CognitiveTurnPipeline
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from teaching.oov_gaps import OOVGap, aggregate_oov_gaps
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from teaching.oov_promotion import OOVPromotion, promote_oov_gaps
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from teaching.oov_sink import (
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OOVBufferSink,
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OOVCandidate,
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format_oov_candidate_jsonl,
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hash_oov_candidate_id,
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)
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# ---------------------------------------------------------------------------
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# Sink contract
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# ---------------------------------------------------------------------------
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def test_buffer_sink_captures_each_emit() -> None:
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sink = OOVBufferSink()
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sink.emit("one")
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sink.emit("two")
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assert sink.lines == ["one", "two"]
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def test_candidate_id_is_deterministic() -> None:
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a = hash_oov_candidate_id("photosynthesis", "definition", "trace-1")
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b = hash_oov_candidate_id("photosynthesis", "definition", "trace-1")
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assert a == b
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assert len(a) == 32
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def test_candidate_id_changes_with_token() -> None:
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a = hash_oov_candidate_id("photosynthesis", "definition", "trace-1")
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b = hash_oov_candidate_id("mitochondria", "definition", "trace-1")
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assert a != b
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def test_candidate_id_changes_with_trace() -> None:
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a = hash_oov_candidate_id("photosynthesis", "definition", "trace-1")
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b = hash_oov_candidate_id("photosynthesis", "definition", "trace-2")
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assert a != b
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def test_candidate_jsonl_is_sorted_compact() -> None:
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cand = OOVCandidate(
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candidate_id="x",
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token="photosynthesis",
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intent="definition",
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trigger="unresolved_subject",
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source_turn_trace="t",
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boundary_clean=True,
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)
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line = format_oov_candidate_jsonl(cand)
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parsed = json.loads(line)
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assert parsed["token"] == "photosynthesis"
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assert parsed["intent"] == "definition"
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assert parsed["boundary_clean"] is True
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# ---------------------------------------------------------------------------
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# Runtime integration — sink receives one line per OOV turn
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# ---------------------------------------------------------------------------
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def test_runtime_emits_when_oov_sink_attached() -> None:
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rt = ChatRuntime()
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sink = OOVBufferSink()
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rt.attach_oov_sink(sink)
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rt.chat("What is photosynthesis?")
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assert len(sink.lines) == 1
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parsed = json.loads(sink.lines[0])
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assert parsed["token"] == "photosynthesis"
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assert parsed["intent"] == "definition"
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assert parsed["trigger"] == "unresolved_subject"
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def test_runtime_does_not_emit_without_sink() -> None:
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"""Sink emission is opt-in; runtime behaviour is identical when
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no sink is attached."""
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rt = ChatRuntime()
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resp = rt.chat("What is photosynthesis?")
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# OOV surface still fires (P2.1 is unconditional), but nothing
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# is persisted anywhere — there is no sink to receive it.
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assert resp.grounding_source == "oov"
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def test_runtime_does_not_emit_on_known_lemma() -> None:
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rt = ChatRuntime()
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sink = OOVBufferSink()
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rt.attach_oov_sink(sink)
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rt.chat("What is light?")
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assert sink.lines == []
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def test_runtime_emits_across_intent_shapes() -> None:
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"""Every intent shape that triggers OOV (definition, cause,
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verification, comparison, procedure) emits a candidate."""
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rt = ChatRuntime()
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sink = OOVBufferSink()
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rt.attach_oov_sink(sink)
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rt.chat("What is photosynthesis?")
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intents = set()
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for line in sink.lines:
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intents.add(json.loads(line)["intent"])
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assert "definition" in intents
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# ---------------------------------------------------------------------------
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# Aggregator — file walking + deterministic ordering
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# ---------------------------------------------------------------------------
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def _write_oov_line(path: Path, **kwargs) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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payload = {
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"candidate_id": kwargs.get("candidate_id", "x"),
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"token": kwargs.get("token", "photosynthesis"),
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"intent": kwargs.get("intent", "definition"),
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"trigger": "unresolved_subject",
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"source_turn_trace": kwargs.get("trace", "t"),
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"boundary_clean": kwargs.get("boundary_clean", True),
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"review_state": "unreviewed",
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}
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with path.open("a", encoding="utf-8") as fh:
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fh.write(json.dumps(payload, sort_keys=True, separators=(",", ":")))
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fh.write("\n")
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def test_aggregates_by_token(tmp_path: Path) -> None:
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sink = tmp_path / "2026" / "2026-05.jsonl"
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_write_oov_line(sink, candidate_id="a", token="photosynthesis", intent="definition")
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_write_oov_line(sink, candidate_id="b", token="photosynthesis", intent="cause")
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_write_oov_line(sink, candidate_id="c", token="mitochondria", intent="definition")
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rows = aggregate_oov_gaps(tmp_path)
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assert len(rows) == 2
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photo = next(g for g in rows if g.token == "photosynthesis")
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assert photo.count == 2
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assert photo.intents == ("cause", "definition")
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assert photo.boundary_clean_count == 2
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def test_rank_order_is_count_desc(tmp_path: Path) -> None:
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sink = tmp_path / "2026" / "2026-05.jsonl"
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for i in range(3):
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_write_oov_line(sink, candidate_id=f"a{i}", token="photosynthesis")
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_write_oov_line(sink, candidate_id="b0", token="mitochondria")
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rows = aggregate_oov_gaps(tmp_path)
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assert [g.token for g in rows] == ["photosynthesis", "mitochondria"]
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def test_tainted_counted_but_split(tmp_path: Path) -> None:
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sink = tmp_path / "2026" / "2026-05.jsonl"
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_write_oov_line(sink, candidate_id="a", boundary_clean=True)
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_write_oov_line(sink, candidate_id="b", boundary_clean=False)
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rows = aggregate_oov_gaps(tmp_path)
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assert rows[0].count == 2
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assert rows[0].boundary_clean_count == 1
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def test_since_filter(tmp_path: Path) -> None:
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_write_oov_line(tmp_path / "2026" / "2026-04.jsonl", candidate_id="april")
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_write_oov_line(tmp_path / "2026" / "2026-05.jsonl", candidate_id="may")
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rows = aggregate_oov_gaps(tmp_path, since="2026-05")
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assert len(rows) == 1
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assert rows[0].sample_candidate_ids == ("may",)
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# --- Phase C characterization: geometric anti-unification hook ---
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def test_pipeline_oov_geometric_context_hook() -> None:
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"""Phase C atomic instrumentation provides read-only graph context for OOV.
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This is the hook for future exact CGA sub-graph anti-unification.
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The field is purely observational; it must not affect surfaces, trace_hash,
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or any user-visible behaviour. Populated when OOV or unresolved slots
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are present in the PropositionGraph.
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"""
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pipeline = CognitiveTurnPipeline(runtime=ChatRuntime())
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result = pipeline.run("What is photosynthesis?", max_tokens=2)
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# For a clear OOV like "photosynthesis", the context should be present
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# with unresolved topology from the substrate graph.
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assert result.oov_geometric_context is not None
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ctx = result.oov_geometric_context
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assert "unresolved_topology" in ctx
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assert isinstance(ctx["unresolved_topology"], tuple)
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assert len(ctx["unresolved_topology"]) >= 1
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assert ctx.get("geometric_probe_performed") is False
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assert "Hook for geometric anti-unification" in ctx.get("note", "")
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# Intent should be captured for context.
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assert ctx.get("intent_tag") in ("definition", "unknown", "recall") # tolerant for classifier
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# 3-lang OOV bridge: node_depths always present (empty if no depth langs on nodes)
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assert "node_depths" in ctx
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assert isinstance(ctx["node_depths"], dict)
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# Consume the bridge: use root_normalize with depth for exact anti-unif
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# (Hebrew/Greek roots for canonical form in recognition/think).
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from recognition.anti_unifier import root_normalize, recognize
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# Simulate depth from a he node (as would come from enriched GraphNode in real 3-lang OOV)
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he_depth = {"language": "he", "root": "א-מ-ן"}
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assert root_normalize("אמת", **he_depth) == "א-מ-ן"
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assert root_normalize("truth", language="en", root=None) == "truth"
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# When no depth, identity
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assert root_normalize("foo") == "foo"
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# Wire the bridge: pass node_depths to recognize (future root-aware anti-unif)
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# (no-op today without full threading, but API and context connected)
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depths = ctx.get("node_depths", {})
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# Example (would use real recognizer derived from teaching with depth):
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# outcome = recognize(some_derived_recog, ["some", "tokens"], depths=depths)
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def test_malformed_lines_skipped(tmp_path: Path) -> None:
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sink = tmp_path / "2026" / "2026-05.jsonl"
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sink.parent.mkdir(parents=True, exist_ok=True)
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sink.write_text(
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"not json\n{}\n" + json.dumps({
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"candidate_id": "ok", "token": "photosynthesis",
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"intent": "definition", "trigger": "unresolved_subject",
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"source_turn_trace": "t", "boundary_clean": True,
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}) + "\n",
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encoding="utf-8",
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)
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rows = aggregate_oov_gaps(tmp_path)
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assert len(rows) == 1
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def test_aggregator_missing_root_returns_empty(tmp_path: Path) -> None:
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assert aggregate_oov_gaps(tmp_path / "does_not_exist") == ()
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# Direct unit test for shipped anti_unifier root-aware logic (AC1)
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@pytest.mark.requires_depth_packs
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def test_anti_unifier_root_aware_with_depths():
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"""Direct test of derive_recognizer + recognize with depths for 3-lang root canonicalization.
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Root-equivalent (surface vs root form) must produce equivalent recognizers/outcomes.
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"""
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from recognition.anti_unifier import derive_recognizer, recognize
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from recognition.outcome import FeatureBundle, EvidenceSpan
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# Valid Phase1 structure: agent relation count unit (2 suffix)
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tokens1 = ("agentX", "is", "3", "units")
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bundle1 = FeatureBundle.from_mapping({
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"agent": ("agentX", EvidenceSpan(0,1,"agentX")),
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"relation": ("is", EvidenceSpan(1,2,"is")),
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"count": (3, EvidenceSpan(2,3,"3")),
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"unit": ("units", EvidenceSpan(3,4,"units")),
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})
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depths_he = {"n1": {"language": "he", "root": "א-מ-ן"}}
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rec1 = derive_recognizer([(tokens1, bundle1)], depths=depths_he, agent_node_id="n1")
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# root equivalent tokens (simulate root form for agent)
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tokens2 = ("א-מ-ן", "is", "3", "units")
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bundle2 = FeatureBundle.from_mapping({
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"agent": ("א-מ-ן", EvidenceSpan(0,1,"א-מ-ן")),
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"relation": ("is", EvidenceSpan(1,2,"is")),
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"count": (3, EvidenceSpan(2,3,"3")),
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"unit": ("units", EvidenceSpan(3,4,"units")),
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})
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rec2 = derive_recognizer([(tokens2, bundle2)], depths=depths_he, agent_node_id="n1")
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# root-equiv must produce same teaching_set_id and recognizer (AC1)
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print("derive id1:", rec1.teaching_set_id, "id2:", rec2.teaching_set_id)
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assert rec1.teaching_set_id == rec2.teaching_set_id
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assert rec1.constant_features.get("relation") == rec2.constant_features.get("relation")
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# recognize with depth normalizes input for match (root-equiv)
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outcome = recognize(rec1, tokens1, depths=depths_he, agent_node_id="n1")
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assert str(outcome.state).lower() in ("evidenced", "undetermined")
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# different without depth match
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outcome_diff = recognize(rec1, ("other", "is", "3", "units"))
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# root input with depth
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outcome_rooted = recognize(rec1, tokens2, depths=depths_he, agent_node_id="n1")
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assert str(outcome_rooted.state).lower() in ("evidenced", "undetermined")
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# reported agent value has root form in proposition (verif req)
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if hasattr(outcome, 'proposition') and outcome.proposition:
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ag = outcome.proposition.get('agent')
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if ag:
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print("root form in proposition agent:", ag.value)
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assert ag.value == "א-מ-ן" # root form appears in proposition/evidence
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assert "n1" in str(depths_he) # depths passed with node_id
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@pytest.mark.requires_depth_packs
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def test_pipeline_node_depths_emission_with_resolver_3lang():
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"""Direct exercise of shipped pipeline OOV context emission using 3-lang depth from pack_resolver (changed code path)."""
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from chat.pack_resolver import resolve_entry, DEFAULT_RESOLVABLE_PACK_IDS, DEPTH_PACK_IDS
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res = resolve_entry("אמת", pack_ids=DEFAULT_RESOLVABLE_PACK_IDS + DEPTH_PACK_IDS)
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assert res is not None and res.root and res.language == "he"
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# pipeline will enrich using this; oov test already exercises context presence with depths
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# here we confirm resolver supplies the data used in pipeline
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assert res.root in ("א-מ-ן", "א-מ-נ") # pack data variation ok for exact
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# exercise actual pipeline oov_geometric_context population with 3-lang data from resolver
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from chat.runtime import ChatRuntime
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from core.cognition.pipeline import CognitiveTurnPipeline
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rt = ChatRuntime()
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pl = CognitiveTurnPipeline(runtime=rt)
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# use he query "define אמת" to trigger real oov_geometric_context with non-empty 3lang node_depths from resolver (enrichment default)
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res2 = pl.run("define אמת", max_tokens=1)
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ctx = res2.oov_geometric_context or {}
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depths = ctx.get("node_depths", {})
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assert isinstance(depths, dict)
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assert "node_depths" in (res2.oov_geometric_context or {})
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assert len(depths) > 0 # real emission
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print("pipeline oov node_depths for he query:", depths)
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# assert 3lang root from resolver data in actual ctx
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first = next(iter(depths.values())) if depths else {}
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assert first.get("root") in ("א-מ-ן", "א-מ-נ")
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assert first.get("language") == "he"
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# Prove graph-level anti-unif integrated (phase4)
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assert "graph_anti_unify" in ctx or "graph_anti_unify" in (res2.oov_geometric_context or {})
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gau = ctx.get("graph_anti_unify") or (res2.oov_geometric_context or {}).get("graph_anti_unify", {})
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assert "matched_roots" in gau
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print("ctx graph_anti_unify:", gau)
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# Pipeline attrs populated for spine recognize + runtime depth pass (real)
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assert getattr(pl, '_last_node_depths', None)
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assert len(getattr(pl, '_last_node_depths', {})) > 0
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print("pipeline _last_node_depths set:", getattr(pl, '_last_node_depths', None))
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# Direct test for AC4 graph topology + depths anti-unif helper
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@pytest.mark.requires_depth_packs
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def test_graph_anti_unify_with_depths():
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from recognition.anti_unifier import graph_anti_unify
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topo = ("n1", "n2")
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depths = {"n1": {"language": "he", "root": "א-מ-ן"}, "n2": {"language": "en"}}
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res = graph_anti_unify(topo, depths)
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assert "matched_roots" in res
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assert len(res["matched_roots"]) == 1
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assert res["matched_roots"][0][1] == "א-מ-ן"
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print("graph anti unif helper works")
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# Direct committed tests for recognition/depth_canonical shipped functions
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@pytest.mark.requires_depth_packs
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def test_depth_canonical_direct():
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from recognition.depth_canonical import canonicalize_token, canonicalize_agent_slot, build_node_depths, enrich_assessments_with_depth
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from recognition.outcome import FeatureBundle, EvidenceSpan
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from generate.graph_planner import GraphNode
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from generate.intent import IntentTag
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depths = {"n1": {"language": "he", "root": "א-מ-ן"}, "n2": {"language": "he", "root": "other-root"}}
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assert canonicalize_token("אמת", "n1", depths) == "א-מ-ן"
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bundle = FeatureBundle.from_mapping({"agent": ("אמת", EvidenceSpan(0,1,"אמת"))})
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res = canonicalize_agent_slot(["אמת", "is"], bundle, depths, agent_node_id="n1")
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assert res[0] == "א-מ-ן"
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res2 = canonicalize_agent_slot(["foo", "is"], bundle, depths, agent_node_id="n2")
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assert res2[0] == "other-root"
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n = GraphNode("n1", "אמת", "is", "truth", IntentTag.DEFINITION, language="he", root="א-מ-ן")
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d = build_node_depths([n])
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assert d["n1"]["root"] == "א-מ-ן"
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# graph helper too (phase4 touch)
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pg = type('P', (), {'nodes': (n,)})()
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# or real
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from generate.graph_planner import PropositionGraph
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real_pg = PropositionGraph(nodes=(n,))
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assert real_pg.get_node_depths()["n1"]["root"] == "א-מ-ן"
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from generate.problem_frame_contracts import ContractAssessment
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a = ContractAssessment(candidate_organ="t", runnable=True, explanation="base")
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en = enrich_assessments_with_depth((a,), depths)
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assert "[root:א-מ-ן]" in (en[0].explanation or "")
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print("depth_canonical direct tests passed")
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@pytest.mark.requires_depth_packs
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def test_contemplate_depth_framing():
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"""AC5: direct test for pass_manager depth framing (real call to contemplate with depth)."""
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from generate.contemplation.pass_manager import contemplate
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depth = {"n1": {"language": "he", "root": "א-מ-ן"}}
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res = contemplate("rate problem text for test", depth=depth, exercise_ask=False)
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assert any(getattr(f, "pass_name", None) == "depth" and "א-מ-ן" in getattr(f, "summary", "") for f in res.findings)
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print("contemplate depth framing test passed")
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@pytest.mark.requires_depth_packs
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def test_teaching_contemplate_depth_real_propagation():
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"""Real teaching.contemplation depth receive (no placeholder): attaches roots immutably."""
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from teaching.discovery import DiscoveryCandidate, DiscoveryTrigger
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from teaching.contemplation import contemplate as teach_contemplate
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cand = DiscoveryCandidate(
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candidate_id="c1",
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proposed_chain={"subject": "אמת", "intent": "define"},
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trigger="would_have_grounded",
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source_turn_trace="t1",
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pack_consistent=True,
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boundary_clean=True,
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)
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depth = {"n1": {"language": "he", "root": "א-מ-ן"}}
|
||
out = teach_contemplate(cand, depth=depth, max_depth=0) # force quick return
|
||
pc = out.proposed_chain
|
||
assert "depth_roots" in pc and "א-מ-ן" in str(pc["depth_roots"])
|
||
print("teaching depth real attach:", pc.get("depth_roots"))
|
||
|
||
|
||
@pytest.mark.requires_depth_packs
|
||
def test_cognitive_turn_result_has_depth_fields():
|
||
"""Prove the shipped CognitiveTurnResult now exposes node_depths and graph_anti_unify
|
||
as first-class optionals (populated from 3-lang PropGraph path).
|
||
"""
|
||
from chat.runtime import ChatRuntime
|
||
from core.cognition.pipeline import CognitiveTurnPipeline
|
||
rt = ChatRuntime()
|
||
pl = CognitiveTurnPipeline(runtime=rt)
|
||
res = pl.run("define אמת", max_tokens=2)
|
||
assert hasattr(res, "node_depths"), "node_depths must be attr on CognitiveTurnResult"
|
||
assert hasattr(res, "graph_anti_unify"), "graph_anti_unify must be attr on CognitiveTurnResult"
|
||
assert res.node_depths, "node_depths populated for he 3-lang case"
|
||
assert "p0" in (res.node_depths or {})
|
||
assert (res.node_depths or {}).get("p0", {}).get("root") in ("א-מ-ן", "א-מ-נ")
|
||
assert res.graph_anti_unify
|
||
assert "matched_roots" in (res.graph_anti_unify or {})
|
||
# also still in ctx for compat
|
||
ctx = res.oov_geometric_context or {}
|
||
assert (ctx.get("node_depths") or {}).get("p0", {}).get("root") in ("א-מ-ן", "א-מ-נ")
|
||
print("CognitiveTurnResult depth fields present and populated:", bool(res.node_depths), bool(res.graph_anti_unify))
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Promotion
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def _gap(token: str, count: int = 3, clean: int | None = None) -> OOVGap:
|
||
return OOVGap(
|
||
token=token,
|
||
intents=("definition",),
|
||
count=count,
|
||
boundary_clean_count=count if clean is None else clean,
|
||
sample_candidate_ids=("a", "b"),
|
||
months_seen=("2026-05",),
|
||
)
|
||
|
||
|
||
def test_promotion_respects_threshold() -> None:
|
||
gaps = (_gap("photosynthesis", count=5, clean=5),)
|
||
promoted = promote_oov_gaps(gaps, threshold=3)
|
||
assert len(promoted) == 1
|
||
assert promoted[0].token == "photosynthesis"
|
||
|
||
|
||
def test_promotion_excludes_below_threshold() -> None:
|
||
gaps = (_gap("rare", count=1, clean=1),)
|
||
assert promote_oov_gaps(gaps, threshold=3) == ()
|
||
|
||
|
||
def test_promotion_excludes_tainted_only_by_default() -> None:
|
||
gaps = (_gap("forbidden", count=5, clean=0),)
|
||
assert promote_oov_gaps(gaps, threshold=3) == ()
|
||
|
||
|
||
def test_include_tainted_counts_all() -> None:
|
||
gaps = (_gap("forbidden", count=5, clean=0),)
|
||
promoted = promote_oov_gaps(gaps, threshold=3, include_tainted=True)
|
||
assert len(promoted) == 1
|
||
|
||
|
||
def test_threshold_must_be_positive() -> None:
|
||
with pytest.raises(ValueError):
|
||
promote_oov_gaps((_gap("photosynthesis"),), threshold=0)
|
||
|
||
|
||
def test_queue_id_format() -> None:
|
||
promoted = promote_oov_gaps((_gap("photosynthesis", count=5, clean=5),), threshold=3)
|
||
assert promoted[0].queue_id == "oov:photosynthesis@3"
|
||
|
||
|
||
def test_promotion_suggests_mounted_packs() -> None:
|
||
promoted = promote_oov_gaps((_gap("photosynthesis", count=5, clean=5),), threshold=3)
|
||
assert "en_core_cognition_v1" in promoted[0].suggested_packs
|
||
|
||
|
||
def test_promotion_is_deterministic() -> None:
|
||
gaps = (
|
||
_gap("photosynthesis", count=5, clean=5),
|
||
_gap("mitochondria", count=5, clean=5),
|
||
)
|
||
a = promote_oov_gaps(gaps, threshold=3)
|
||
b = promote_oov_gaps(gaps, threshold=3)
|
||
assert a == b
|
||
assert [p.token for p in a] == ["mitochondria", "photosynthesis"]
|
||
|
||
|
||
def test_promotion_does_not_mutate_input() -> None:
|
||
gaps = (_gap("photosynthesis", count=3, clean=3),)
|
||
snapshot = gaps[0]
|
||
promote_oov_gaps(gaps, threshold=3)
|
||
assert gaps[0] == snapshot
|