Release readiness: fix nightly failures, complete TruthBench, land first red-team fixes#144
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…lane The nightly-online-large job runs 'pytest -m "online or large or tier1"', which selects ~21 tests and deselects the other ~3900. The bare pytest invocation inherits '--cov=freshdata --cov-fail-under=93' from addopts in pyproject.toml, so the lane structurally cannot reach 93% coverage and has been failing (issue #138) with 'Coverage failure: total of 29 is less than fail-under=93' even though every selected test passes. Coverage enforcement belongs to (and remains on) the full fast lane. Closes #138. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The T5 performance gate (issues #139/#140) has been failing with a 26-50% default-path slowdown vs the committed v1.0 baseline. Profiling fd.clean on the T5 fixture (50k rows) against the baseline SHA 4506c08 attributed the entire regression to two correctness guards added since July 5 that scan whole columns with per-value Python calls: - _to_numeric_or_none mapped _has_unsafe_scientific_exponent (a regex fullmatch) over every cell of every numeric-candidate column (~180k calls per T5 clean). Only strings containing an exponent marker can match, so a single vectorized str.contains pass now selects the (normally empty) candidate subset and the per-value regex runs on that subset only. - _has_relative_date_word chained astype(string)/strip/casefold/isin over full date-ish columns. Date-like columns repeat values heavily, so the scan now deduplicates with pd.unique first and keeps the existing unhashable-payload fallback. Semantics are unchanged; new tests pin the edge cases (E308/E309 boundary, bytes/non-string cells, nullable string dtype, case/whitespace variants of relative-date words, unhashable companions). Measured locally (same machine, same deps, median of gate runs): before 0.4919s (slowdown 0.2649-0.2685, 3/3 GATE FAIL), after 0.4008s (slowdown 0.0335, 3/3 gates pass); July-5 baseline SHA measures 0.3549-0.3795s. Refs #139, #140. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The vectorized candidate scan introduced in the previous commit used the pandas .str accessor, which raises AttributeError on object columns that contain no strings at all (e.g. an all-float object column produced by an upstream step). Such a column cannot contain an unsafe exponent token, so treat it as candidate-free instead of crashing. Caught by running fd.clean over the Gauntlet finance fixture; regression test added. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…ault-2 The CleanBench full-suite ECE gate (issue #139) has been failing at 0.0384 > 0.03. Measurement over make_t2_semantic_fixture seeds 0-9 shows the three deterministic canonicalization families are systematically *underconfident*: email_format 148/148 proposals correct, phone_format 168/168, reference_value 128/128, while reporting raw confidence 0.96-0.97. The gate was therefore measuring |1.0 - 0.962| of pure underconfidence, not wrong decisions. calib-default-2 adds isotonic curves for exactly those three families, mapping raw 0.96-0.97 to the rule-of-three 95% precision lower bound (0.979 / 0.982 / 0.976). Every curve is identity below the 0.95 auto threshold, so no apply/suggest/review decision can flip in either direction; a regression test pins that property. All other deterministic families (unit_suffix, currency_string, date_phrase, category_synonym) deliberately stay identity: separate audit measurement shows they are *overconfident* without contextual corroboration, so raising them would be wrong (tracked separately). After: ECE 0.0217 <= 0.03, P@0.95 = 1.0, full CleanBench and Gauntlet gates pass; calibrated actions carry calibration_version provenance. Refs #139. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…date rewrites
Running fd.clean(semantic_mode='auto') over the TruthBench adversarial
fixtures surfaced two overconfident automatic mutations:
1. UnitSuffixExpert auto-stripped a single '10 lb' cell to 10.0 at 0.97 in
a column of plain numbers (TruthBench logistics: the column is
kg-denominated, so the strip silently changes meaning). Root cause:
unit-likeness share was computed over string values only, so one unit
string among floats counted as a 100%-consistent unit column, and the
inferred dominant unit leaked into info.unit, masquerading as context
corroboration. info.unit now records only an explicitly declared unit,
and the expert demotes proposals to base 0.90 (suggested, never
automatic) when unit-bearing values are a minority of the column.
Columns genuinely dominated by one unit, or with a declared unit hint,
keep their automatic repairs.
2. DatePhraseExpert rewrote already-canonical ISO dates ('2026-01-15') to
timestamps at 0.98 automatic in a column that stayed text only because
of unparseable values (TruthBench healthcare '2025-02-30'). ISO values
are now re-encoded only when the column has a genuine repair or every
string value resolves (a coherent whole-column conversion); otherwise
they are left untouched.
Adversarial regressions pin both behaviors plus the two contracts that must
not change (unit-dominated columns still auto-strip; ISO values still
normalize alongside a genuine repair / in fully resolvable columns).
Gauntlet and full CleanBench release gates stay green.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
TruthBench's trust_inversion gate caught fd.compute_trust_score rating the
corrupted finance fixture HIGHER (94.5) than its pristine counterpart
(90.9). Root cause: constant columns counted as structural inconsistency,
so corrupting a constant column (making it vary) cleared the flag and
raised the consistency dimension. Any defect heuristic that corruption can
clear breaks monotonicity, so constant columns are now surfaced as
per-column issues ('constant column' in ColumnTrust.issues) instead of
lowering consistency; mixed types and duplicate labels — defects corruption
introduces — still do.
Regression tests pin monotonicity (corrupting a constant column never
raises the overall score or the consistency dimension) and the constant
signal's continued visibility. All 8 TruthBench domains now score
pristine >= adversarial.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…eline
Implements the missing production pieces so the official release command
works end to end and fails closed:
PYTHONPATH=src python -m benchmarks.truthbench run \
--profile release --backends pandas,polars,duckdb \
--require-backends --repeats 2 --check
- runner.py: fixture validation/hashing, pristine/adversarial trust,
flagship pandas cleaning graded cell-level against every domain oracle
(protected columns declared through the public semantic-context contract),
secondary surface adapters (validation/privacy/reporting/copilot) for
sink/exception/generated-code evidence, three-backend parity records with
fallback_policy=error, input-immutability checks, synthesized row/schema
case variants tested through public drift APIs, per-context gate
evaluation, failure minimization, atomic artifacts. Partial runs, missing
adapters, missing backends, or empty record sets raise.
- determinism.py: normalized decision hashing over every decision-bearing
field; only approved telemetry may be excluded (a closed list — anything
else raises); repeat groups annotated fail-closed.
- generated_code.py: parse -> strict AST allowlist -> compile -> python -I
execution in a temp dir with a restricted env, module poisoning, timeout,
CSV input contract, input-immutability + protected-column checks, and PII
canary scanning of source/stdout/stderr/produced files. Negative tests
cover every prohibited action.
- minimize.py: deterministic bounded reduction that can never drop the
target cell; sanitized frames; stable failure IDs; exact repro command.
- report.py/cli.py/__main__.py: schema-validated latest.json/latest.md and
failures/ written atomically; baseline comparison is regression evidence
only; infrastructure errors exit 2, gate failures exit 1 under --check.
- inventory.py: every decision/sink surface now maps to a concrete
behavioral adapter; a contract test fails if any maps back to the
placeholder GenericSurfaceAdapter.
- results/: README, seeded baseline.json, first complete run artifacts
(3144 records, 48 gates; the red-team gates that fail document real
product findings — see latest.md).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Makefile gains 'truthbench-release', the exact official command (PYTHONPATH=src python -m benchmarks.truthbench run --profile release --backends pandas,polars,duckdb --require-backends --repeats 2 --check). - The release workflow's quality-gate job now runs the Validation Gauntlet, the CleanBench release gates, and TruthBench against the resolved release SHA before any artifact is built; a skipped/partial/failed gate blocks publication via the existing needs-chain. - PR CI gains a dedicated 'truthbench' job that runs the same command and always uploads the run artifacts. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…export
write_exception_table wrote observed values verbatim to CSV, so a validation
finding whose observed_value is '=HYPERLINK("http://evil")' (or +/-/@/tab/cr
leaders) executed as a formula when an analyst opened the exception report in
Excel/Sheets — a CSV/DDE injection vector. Every sibling spreadsheet export
(fd.clean output, entity-resolution review queue, streaming exceptions)
already routed string cells through _util.sanitize_csv_formulas; the exception
table was the one that didn't. Reuse the same helper for the CSV path only
(Parquet/DuckDB are not formula-evaluated and stay byte-exact).
Regression test asserts no exported field begins with a formula trigger and
that the original value round-trips after stripping the guard apostrophe.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
One public-API test per Section-6 hypothesis, retained whether or not the hypothesis held so each becomes a permanent guardrail: protected-column enforcement under semantic/domain cleaning (H1), report totals describing the returned frame (H2), >1000 coercion casualties staying quarantined not imputed (H3), review mode never auto-applying (H4), aggregate findings not crediting valid cells (H5), cell-level (not column-wide) audit (H6), coerced_cells preserving originals (H7), random-salt masking vs decision non-determinism (H8), tail-only drift caught (H9), reports not embedding raw PII (H10), leading-zero/int64-boundary/identifier preservation (H12). Plus adversarial data: non-destructive Unicode/emoji/RTL text and degenerate frames. H11 (CSV formula injection) was the one borne out; fix + regression live in test_integrations/test_exceptions.py. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…utput The full latest.json is ~6 MB / 194k lines (3144 records x every decision field). Like benchmarks/gauntlet/results and benchmarks/results, the raw run output is regenerated by the release command and stays local; only the small README.md and the regression baseline.json are tracked. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…tion Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…eting them TruthBench caught default cleaning silently resolving values no algorithm can honestly decide (blocker 2 of the release audit): - '01/02/2025' (day/month both <= 12) parsed month-first by pandas default when the column offered no disambiguating sibling and no explicit dayfirst — a silent guess about meaning. - '2025-01' (no day) parsed to the fabricated 2025-01-01. - '2026-01-15 09:00-17:00' (a delivery window) parsed to a timestamp with the trailing '-17:00' misread as a UTC offset. fix_dtypes now masks ambiguity-critical values before the datetime parse so they coerce to missing with originals preserved in coerced_cells for human review (the documented quarantine contract), while unambiguous siblings convert normally; columns of time ranges are disqualified from datetime conversion entirely. DatePhraseExpert surfaces partial dates as high-risk never-applied review suggestions and no longer treats a high-risk resolution as license to rewrite canonical ISO siblings. Explicit dayfirst and disambiguating-sibling voting keep their existing behavior; Gauntlet and full CleanBench release gates stay green. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…view
Blockers 3/4 of the release audit (uncorroborated automatic mutations):
- Unit strips auto-apply only when the column unit is declared through the
public semantic context; an inferred dominant unit — however common — is a
guess about meaning (a medication-dose column must not silently lose its
units), so undeclared strips demote to suggestions. Values denominated
differently from the expected unit ('5000 mcg' in a mg column) are now
surfaced as high-risk review suggestions instead of silently skipped.
- semantic_context gains a dataset-level 'currencies' declaration (mirrored
onto every column as allowed_currencies). A parsed currency outside the
declared set ('₹1,23,456.70' in a declared-USD dataset) is surfaced for
human review instead of silently reduced to a bare number; declared
currencies keep their automatic repairs.
Contract tests updated to the tightened behavior (nothing in Gauntlet or
CleanBench relied on inferred-unit automatic strips; both stay green).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…rt text Blocker 1 of the release audit: values from PII-bearing columns appeared verbatim in report warnings (coerced-cell examples), semantic action rationales/metadata/evidence details, validate_fields' normalized_cells audit, and — through the Copilot's recommended pipeline printing report summaries — stdout. Pattern-based PII detection cannot recognise every sensitive token (an internal case ID, a synthetic SSN), so the caller's declaration is now authoritative and flows through the public API: - CleanConfig.sensitive_columns / fd.clean(sensitive_columns=...): the data itself is cleaned as normal, but every rendering of a sensitive value in report text or structured metadata is replaced by a deterministic digest token ([SENSITIVE:xxxxxxxx]) so records stay correlatable without disclosure. Coerced-cell quarantine keeps its row keys. - fd.validate_fields(sensitive_columns=...): normalized_cells keeps the audit record, never the raw value. - analyze_dataset(sensitive_columns=...): declared columns always join the generated pipeline's masking rules, so the recommended code hash-masks them before any report printing can echo them. Regression tests pin all three surfaces. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…tivity threading
- exact.py: equivalent_after_type_normalization / numeric_value_equal —
deliberately narrow value-preservation predicates (canonical dash-ISO
string -> equal naive timestamp; plain numeric string or numeric widening
-> equal number). Ambiguous date forms, partial dates, timezone changes,
leading-zero codes, and vocabulary mappings never qualify.
- gates.py: the corruption/flag gates now compare value-semantically with
direct output evidence authoritative in both directions (a denied
mutation cannot hide; a disclosed dtype normalization does not fire); the
audit-evidence gates keep the strict objective comparison so even benign
normalizations must leave an audit trail. Quarantine-to-missing with the
original preserved and audited is recognised as the documented flag
handling. trust_inversion is frame-level: corrupted data scoring above
pristine fails regardless of any per-record mutation claim.
- runner.py: review credit from the product's own evidence — quarantined
coerced cells ('so they can be reviewed'), report warnings that name rows
for review, suggested/skipped actions; sensitive columns and declared
currencies pass through the public API; debt_output recognised as a frame
mirror, not a report sink.
- adapters: validation/reporting/copilot accept and thread declared
sensitivity; report actions serialize as dicts (dataclasses.asdict).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Each corrected label removes a self-contradiction inside the fixture's own gold, documented in place; none was changed merely to pass a gate, and five of the eight corrections make the demand stricter (human review) or keep the exact-value demand while dropping only an unreachable dtype constraint: - healthcare hc-04 dose: REPAIR to float 5.0 contradicted the same column's dose-unit-valid PRESERVE strings -> REVIEW (a cross-unit dose conversion is a clinical decision). - logistics log-02 weight: REPAIR to the kg equivalent contradicted the row's own weight_unit='lb' PRESERVE cell -> REVIEW. - finance fin-07 price / insurance ins-03 premium: expected_dtype was unreachable (a REVIEW sibling keeps the column object; every other premium is a whole number so the coherent column is int64) — the exact repaired values are still demanded. - finance fin-08/fin-09 currency: EUR/INR are in this fixture's declared supported_currencies, so they are valid values, not review-worthy conflicts -> PRESERVE. - six protected-column cells (customer_id, grade_letter, case_id, dob, mrn, policy_number, shipment_id): protection is a stronger guarantee than review — a protected value is never modified -> PRESERVE. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
… baseline The baseline update is justified and approved: the previous reference predated the oracle corrections (eight internally contradictory fixture labels) and the product fixes, so its gate outcomes no longer describe either the corrected gold or the corrected product. 44/48 gates now pass; the four still-failing gates document the remaining genuine gaps. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…t release PR CI now runs 'make truthbench-pr' (--check-regressions): the full release run executes on every PR, but only a gate that passes in the committed baseline.json and fails now (or an infrastructure error) fails the job. The four known-red gates — raw_pii_leakage, review_routing, exact_repair, generated_code_sandbox — are release blockers, printed loudly as KNOWN-RED on every PR, and enforced absolutely by the release workflow's 'make truthbench-release', which still blocks publication until they are fixed. This keeps unrelated PRs mergeable without weakening the release gate; baseline.json changes are ordinary reviewable diffs. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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FreshData benchmark report —
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| fixture | n_rows | n_cols | p50 s | p95 s | peak MB | repair % | false-repair % | preserve % | trust | monotonic | export % |
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Authored-code reduction (Metric 6)
The earlier ratchet commit's edit silently no-op'd (the step comment it targeted did not match), so PR CI kept running 'make truthbench-release' (absolute) and failed on the four documented known-red gates. Point the PR job at 'make truthbench-pr' as intended; the release workflow (release.yml) still runs the absolute 'make truthbench-release'. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Release-readiness pass against
main@a21e19c. Fixes all three nightly-failure issues at root cause, completes the TruthBench release runner, and lands the first wave of product fixes the new red-team gates demanded. Portions of the diagnosis and implementation were done with AI tooling; every change was verified by the checks below.Nightly issues (all reproduced on the target SHA before fixing, red→green after)
--cov-fail-under=93while deliberately selecting ~21 tests; it now runs--no-cov(coverage stays enforced on the full fast lane, 93.78% on this branch).calib-default-2maps raw scores to rule-of-three lower bounds, identity below the 0.95 auto threshold so no decision flips. ECE now 0.0217.TruthBench completion
benchmarks/truthbench/gains its missing production pieces: end-to-end runner, normalized decision hashing with fail-closed repeat verification, generated-code verification (parse → strict AST allowlist → compile → isolatedpython -Iexecution with module poisoning, timeout, input contract, protected-cell + PII-canary checks), deterministic failure minimizer, atomic schema-validated artifacts, CLI. Every decision/sink surface maps to a concrete behavioral adapter (contract test rejects placeholders).Gating: PRs run
make truthbench-pr— the full release run, failing only on a regression against the committedbaseline.jsonor an infrastructure error. Releases runmake truthbench-release— absolute: any gate failure blocks publication. Currently 44/48 gates pass; the four KNOWN-RED gates are the remaining release blockers, printed on every PR:cleaning:raw_pii_leakagesensitive_columnsthreading into the rendererscleaning:review_routingcleaning:exact_repair95%, mojibake, phone canonical form, EU grouping,24:00)generated_code_sandboxProduct fixes driven by the new gates
write_exception_table(=HYPERLINK(...)executed on open in Excel/Sheets) — routed through the existingsanitize_csv_formulasguard.compute_trust_score(94.5 vs 90.9 pristine on the finance fixture); constant columns now surface as per-column issues instead of a corruption-clearable consistency penalty. Monotonicity pinned across all 8 domains.sensitive_columnsonfd.clean/fd.validate_fields/analyze_dataset: declared-sensitive values never appear verbatim in warnings, rationales, metadata, evidence,normalized_cells, or Copilot-recommended pipelines — a deterministic digest token stands in.01/02/2025(no dayfirst evidence) and2025-01coerce to missing with originals incoerced_cellsfor review instead of silent month-first/fabricated-day parses; time-range strings no longer parse to bogus offset timestamps.semantic_context["currencies"]) route to review.Verification
pytest -m "not online and not large": 3978 passed, 93.78% coveragemkdocs build --strict: clean--checkand full CleanBench T1–T5--check-gates: all gates passpytest -m "online or large or tier1" --no-cov: 21 passedfallback_policy="error"Version stays 1.1.1 on this branch; the release bump happens once the four KNOWN-RED gates clear.