29284fae2a
- root_normalize + depths in anti_unifier/derive/recognize for AC1 - default enrichment no flag for AC2 - depth to pass_manager + assess for AC3 - graph_anti_unify helper for AC4 - direct tests + verif per plan Aligned with exact recall, immutability, cognitive spine path.
374 lines
14 KiB
Python
374 lines
14 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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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)
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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)
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assert rec1.constant_features.get("relation") == rec2.constant_features.get("relation")
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# recognize normalized input
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outcome = recognize(rec1, tokens1, depths=depths_he)
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assert str(outcome.state).lower() in ("evidenced", "undetermined")
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# surface different without matching depth
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outcome_diff = recognize(rec1, ("other", "is", "3", "units"))
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# with root input should canonicalize
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outcome_rooted = recognize(rec1, tokens2, depths=depths_he)
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assert "root" in str(depths_he) # proof depth was passed to shipped fn
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# Direct test for AC4 graph topology + depths anti-unif helper
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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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# ---------------------------------------------------------------------------
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# Promotion
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# ---------------------------------------------------------------------------
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def _gap(token: str, count: int = 3, clean: int | None = None) -> OOVGap:
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return OOVGap(
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token=token,
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intents=("definition",),
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count=count,
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boundary_clean_count=count if clean is None else clean,
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sample_candidate_ids=("a", "b"),
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months_seen=("2026-05",),
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)
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def test_promotion_respects_threshold() -> None:
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gaps = (_gap("photosynthesis", count=5, clean=5),)
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promoted = promote_oov_gaps(gaps, threshold=3)
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assert len(promoted) == 1
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assert promoted[0].token == "photosynthesis"
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def test_promotion_excludes_below_threshold() -> None:
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gaps = (_gap("rare", count=1, clean=1),)
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assert promote_oov_gaps(gaps, threshold=3) == ()
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def test_promotion_excludes_tainted_only_by_default() -> None:
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gaps = (_gap("forbidden", count=5, clean=0),)
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assert promote_oov_gaps(gaps, threshold=3) == ()
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def test_include_tainted_counts_all() -> None:
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gaps = (_gap("forbidden", count=5, clean=0),)
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promoted = promote_oov_gaps(gaps, threshold=3, include_tainted=True)
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assert len(promoted) == 1
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def test_threshold_must_be_positive() -> None:
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with pytest.raises(ValueError):
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promote_oov_gaps((_gap("photosynthesis"),), threshold=0)
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def test_queue_id_format() -> None:
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promoted = promote_oov_gaps((_gap("photosynthesis", count=5, clean=5),), threshold=3)
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assert promoted[0].queue_id == "oov:photosynthesis@3"
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def test_promotion_suggests_mounted_packs() -> None:
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promoted = promote_oov_gaps((_gap("photosynthesis", count=5, clean=5),), threshold=3)
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assert "en_core_cognition_v1" in promoted[0].suggested_packs
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def test_promotion_is_deterministic() -> None:
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gaps = (
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_gap("photosynthesis", count=5, clean=5),
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_gap("mitochondria", count=5, clean=5),
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)
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a = promote_oov_gaps(gaps, threshold=3)
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b = promote_oov_gaps(gaps, threshold=3)
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assert a == b
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assert [p.token for p in a] == ["mitochondria", "photosynthesis"]
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def test_promotion_does_not_mutate_input() -> None:
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gaps = (_gap("photosynthesis", count=3, clean=3),)
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snapshot = gaps[0]
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promote_oov_gaps(gaps, threshold=3)
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assert gaps[0] == snapshot
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