Adds _HYBRID_BERT_LABEL_MAP to translate the 7-class output vocabulary of
krishnas4415/log-anomaly-detection-models (Hybrid-BERT, MIT) to Turnstone
SeverityLabel. _map_label now checks the Hybrid-BERT map before the standard
map so either model family works via TURNSTONE_CLASSIFIER_MODEL without any
additional code path.
Mapping (confirmed from model config.json):
normal → INFO
security_anomaly → ERROR
system_failure → CRITICAL
performance_issue → WARN
network_anomaly → WARN
config_error → ERROR
hardware_issue → CRITICAL
Keyword-based CRITICAL promotion and low-confidence DEBUG demotion apply on
top of the base mapping (same rules as the standard vocabulary).
11 new tests covering all 7 Hybrid-BERT labels, case-insensitivity, and
regression on standard-vocabulary labels. 372 tests passing total.
Note: custom loading code for the non-standard .pt checkpoint format is
explicitly out of scope — evaluate better-packaged HF alternatives first
(see #41 for candidate list).
Closes: #41
- #33: Wrap ClassifiedTimeline.cluster_severities in MappingProxyType for
true immutability (frozen=True only blocks field reassignment, not dict
mutation).
- #34: Remove dead suppression branch in synthesizer._build_hypothesis_block.
active[] is already filtered to not rh.suppress, so the 'Yes — suppressed'
branch was unreachable. Now shows novelty score only.
- #35: Extract shared _llm_client.py with call_llm() + extract_content() +
strip_json_fences(). Both RootCauseHypothesizer and SummarySynthesizer
now import from one source. Also strips JSON fences from LLM output before
parsing in hypothesizer._parse_response.
- #36: Add per-stage try/except in pipeline.run_pipeline(). Unhandled
stage exceptions now emit {type: 'error'} + {type: 'done'} SSE events
instead of silently closing the stream.
- #37: Move format_context_block() call inside the legacy LLM branch in
diagnose/__init__.py — it was being computed unconditionally but only
used in the non-pipeline path.
- #38: Coerce supporting_cluster_ids items to str() in hypothesizer
_parse_response to guard against LLMs returning integers instead of
string cluster IDs.
Three-path classification: ML (transformers pipeline, lazy singleton) →
pattern_tags (YAML pattern severity dict) → regex (detect_severity).
- Path A: HF text-classification pipeline loaded lazily on first classify()
call via module-level singleton; shim promotes ERROR+keyword hits to CRITICAL
and demotes low-confidence INFO to DEBUG.
- Path B: maps cluster.pattern_tags through the loaded pattern severity dict;
picks the highest severity across matching tags.
- Path C: falls back to detect_severity() regex scan on representative_text;
defaults to INFO when no keyword matches.
- Pattern file resolved from constructor arg or TURNSTONE_PATTERNS env var
(mirrors app/rest.py convention).
- No crash when transformers is not installed; ImportError on per-cluster ML
inference triggers clean per-cluster fallback to pattern_tags/regex.
- ClassifiedTimeline.classifier_used reflects the primary session path.
Tests (10 new, 328 total, all passing):
- ML ERROR, CRITICAL promotion, DEBUG demotion, WARNING→WARN
- pattern_tags resolution from YAML fixture
- regex ERROR detection and INFO default
- ImportError clean fallback
- empty timeline no-crash
- ClassifiedTimeline FrozenInstanceError on mutation
Closes: #29
