From fcce7f50ac190aacc86c5d5907464d93e25ae91f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Isaac=20D=27C=C3=A9sares?= Date: Fri, 4 Sep 2026 14:52:58 -0300 Subject: [PATCH 1/2] feat: harden packaging, provenance, and validation --- .gitattributes | 12 + .github/dependabot.yml | 10 + .github/workflows/ci.yml | 27 ++ .github/workflows/model-smoke.yml | 22 + AGENTS.md | 68 ++- CHANGELOG.md | 19 + CITATION.cff | 6 +- MASTER_PLAN.md | 201 ++++----- README.md | 208 ++++----- SECURITY.md | 15 + artifacts/contrast_run/run_manifest.json | 40 +- artifacts/corpus_run/run_manifest.json | 33 +- artifacts/provenance/contrast_run.json | 2 +- artifacts/provenance/contrast_run.png | Bin 263156 -> 290878 bytes artifacts/provenance/contrast_run.ttl | 19 +- artifacts/provenance/corpus_run.json | 2 +- artifacts/provenance/corpus_run.png | Bin 220452 -> 243638 bytes artifacts/provenance/corpus_run.ttl | 15 +- artifacts/provenance/first_provenance.json | 2 +- artifacts/provenance/first_provenance.png | Bin 74746 -> 161912 bytes artifacts/provenance/first_provenance.ttl | 26 +- data/README.md | 16 + docs/README.md | 15 +- docs/acknowledgements.md | 2 +- docs/ai-usage.md | 14 +- docs/architecture.md | 27 +- docs/decisions.md | 98 ++-- docs/responsible-use.md | 53 +++ docs/validation.md | 236 +++++----- main.py | 41 -- pyproject.toml | 59 ++- scripts/bingler_baseline.py | 5 +- scripts/evaluate_face_validity.py | 24 +- scripts/first_provenance.py | 26 +- scripts/pin_models.py | 18 - scripts/plot_linguistic_variables.py | 22 +- scripts/provenance_contrast_run.py | 53 ++- scripts/provenance_corpus_run.py | 46 +- scripts/render_provenance_png.py | 15 +- scripts/run_contrast_set.py | 123 +++-- scripts/run_full_corpus.py | 101 ++++- scripts/sanity_check_distributions.py | 21 +- scripts/validation/anchor_verify.py | 2 +- scripts/validation/hash3_characterize.py | 17 +- scripts/validation/integration_seam_test.py | 45 +- scripts/validation/mf_experiment.py | 24 +- scripts/verify_artifact_integrity.py | 80 ++++ scripts/verify_model_revisions.py | 36 ++ src/greenrisk/__init__.py | 14 + src/greenrisk/cli.py | 152 +++++++ .../greenrisk/linguistic_variables.py | 2 +- src/greenrisk/metadata.py | 45 ++ models.py => src/greenrisk/models.py | 69 ++- src/greenrisk/provenance.py | 108 +++++ rule_base.py => src/greenrisk/rule_base.py | 54 ++- tests/property_test_rule_base.py | 13 +- tests/smoke_test_all_models.py | 73 +-- tests/smoke_test_detector_model.py | 33 +- tests/smoke_test_provenance.py | 6 +- tests/test_artifact_integrity.py | 11 + tests/test_cli.py | 49 ++ tests/test_instrument_lock.py | 60 +++ uv.lock | 422 +++++++++++------- 63 files changed, 2008 insertions(+), 1049 deletions(-) create mode 100644 .gitattributes create mode 100644 .github/dependabot.yml create mode 100644 .github/workflows/ci.yml create mode 100644 .github/workflows/model-smoke.yml create mode 100644 CHANGELOG.md create mode 100644 SECURITY.md create mode 100644 data/README.md create mode 100644 docs/responsible-use.md delete mode 100644 main.py delete mode 100644 scripts/pin_models.py create mode 100644 scripts/verify_artifact_integrity.py create mode 100644 scripts/verify_model_revisions.py create mode 100644 src/greenrisk/__init__.py create mode 100644 src/greenrisk/cli.py rename linguistic_variables.py => src/greenrisk/linguistic_variables.py (99%) create mode 100644 src/greenrisk/metadata.py rename models.py => src/greenrisk/models.py (76%) create mode 100644 src/greenrisk/provenance.py rename rule_base.py => src/greenrisk/rule_base.py (76%) create mode 100644 tests/test_artifact_integrity.py create mode 100644 tests/test_cli.py create mode 100644 tests/test_instrument_lock.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..6fa8337 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,12 @@ +* text=auto +*.py text eol=lf +*.md text eol=lf +*.toml text eol=lf +*.json text eol=lf +*.csv text eol=lf +*.ttl text eol=lf +*.yml text eol=lf +*.yaml text eol=lf +*.lock text eol=lf +*.png binary +*.pdf binary diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..7526cd1 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,10 @@ +version: 2 +updates: + - package-ecosystem: github-actions + directory: / + schedule: + interval: monthly + - package-ecosystem: uv + directory: / + schedule: + interval: monthly diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..d22e72b --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,27 @@ +name: CI + +on: + push: + branches: [master] + pull_request: + workflow_dispatch: + +permissions: + contents: read + +jobs: + validate: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5 + - uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6 + with: + enable-cache: true + - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6 + with: + python-version: "3.12" + - run: uv sync --frozen --group dev + - run: uv run ruff check . + - run: uv run pytest -m "not model" + - run: uv run python scripts/verify_artifact_integrity.py + - run: uv build diff --git a/.github/workflows/model-smoke.yml b/.github/workflows/model-smoke.yml new file mode 100644 index 0000000..5d5825e --- /dev/null +++ b/.github/workflows/model-smoke.yml @@ -0,0 +1,22 @@ +name: Pinned model smoke tests + +on: + workflow_dispatch: + +permissions: + contents: read + +jobs: + models: + runs-on: ubuntu-latest + timeout-minutes: 45 + steps: + - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5 + - uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6 + with: + enable-cache: true + - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6 + with: + python-version: "3.12" + - run: uv sync --frozen --extra models --group dev + - run: uv run pytest -m model diff --git a/AGENTS.md b/AGENTS.md index bae04e8..28251cb 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,47 +1,35 @@ # Repository Guidelines -## Project Structure & Module Organization - -GreenRisk is a Python 3.12 project for explainable greenwashing-risk scoring. `MASTER_PLAN.md` is the canonical architecture and roadmap; read it before changing behavior. - -- Core instrument modules are at the repository root: `models.py`, `linguistic_variables.py`, `rule_base.py`, and `main.py`. -- Pipeline scripts live in `scripts/`; locked validation harnesses live in `scripts/validation/`. -- Smoke tests live in `tests/` and are executable Python scripts, not a pytest suite. `tests/property_test_rule_base.py` asserts the locked rule base's documented properties and needs no model download. -- Input data is under `data/`; generated, committed outputs are under `artifacts/`. -- `docs/` is for consolidated public documentation, including `decisions.md` (the public decision record) and the project paper. `development/` is intentionally gitignored process material. - -## Build, Test, and Development Commands - -Use `uv` for dependency management and command execution. - -```powershell -uv sync -uv run python tests/smoke_test_all_models.py -uv run python tests/smoke_test_detector_model.py -uv run python tests/smoke_test_provenance.py -uv run python scripts/validation/integration_seam_test.py -n 20 --gate 0.5 -uv run python scripts/plot_linguistic_variables.py -uv run python scripts/first_provenance.py +`MASTER_PLAN.md` is the canonical repository map. GreenRisk is an installable +Python 3.12 `src`-layout project; package code lives in `src/greenrisk/`, +research producers in `scripts/`, tests in `tests/`, inputs in `data/`, and +committed evidence in `artifacts/`. + +Use `uv` and the lockfile: + +```bash +uv sync --frozen --group dev +uv run ruff check . +uv run pytest -m "not model" +uv run python scripts/verify_artifact_integrity.py +uv build ``` -`uv sync` installs dependencies from `pyproject.toml` and `uv.lock`. Smoke tests verify model loading, detector behavior, and provenance basics. Validation scripts re-check the locked instrument and may load large ClimateBERT models. - -## Coding Style & Naming Conventions - -Follow the existing style: 4-space indentation, type hints where useful, concise docstrings, and explicit data structures for model registries and fuzzy rules. Use `snake_case` for functions and variables, `UPPER_CASE` for constants, and short rule IDs such as `S1`, `N2`, or `G1`. - -Keep the scoring instrument explicit over clever abstraction. Changes to `MODEL_REGISTRY`, `SIGNAL_MAP`, membership functions, or `RULES` affect reproducibility and must be treated as instrument changes. - -## Testing Guidelines - -Run the relevant smoke test before submitting any change. For changes to fuzzy logic, provenance, model mappings, or artifacts, also run the matching script in `scripts/validation/` or the artifact producer you touched. Any change touching `RULES` or the membership functions must also pass `uv run python tests/property_test_rule_base.py`. Do not describe tests as pytest-based unless a pytest suite is added. - -## Commit & Pull Request Guidelines - -Commits use conventional prefixes such as `feat:` and `docs:` with a scoped, imperative summary, for example `feat: add held-out face-validity artifacts`. +Tests marked `model` download and execute the pinned ClimateBERT revisions; +run them with `uv sync --frozen --extra models --group dev` when model adapters +change. Full research scripts require `--extra research`. -Pull requests should include a behavior summary, commands run, artifacts changed, and any linked decision-log entry. Include screenshots only for generated figures or provenance images. +Use 4-space indentation, type hints where useful, concise docstrings, and +explicit data structures. Keep the scoring instrument readable. Input +probabilities must be finite and in `[0,1]`; do not rely on implicit clipping. +Avoid import-time network, login, filesystem, or credential side effects. -## Architecture & Reproducibility Notes +The numerical instrument is frozen at `rulebase-locked-v1`. Any change to +`MODEL_REGISTRY`, `SIGNAL_MAP`, membership functions, or `RULES` is an +instrument change and must be a separate logged ablation. Do not rewrite +historical artifacts without running their producer and updating manifest and +PROV-O hashes. Preserve secrets such as `.env` and `HF_TOKEN`. -The instrument is frozen at tag `rulebase-locked-v1`. Any post-lock change to rules, membership functions, model revisions, or signal mappings must be a separate logged ablation, never a silent edit. Preserve pinned ClimateBERT revisions and avoid committing secrets such as `.env` or `HF_TOKEN`. +Use conventional commit prefixes. Pull requests should state behavior changes, +commands run, artifact changes, evidence/claim implications, and any decision +record entry. diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..7e5a8dc --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,19 @@ +# Changelog + +## 0.2.0 — unreleased + +- Package GreenRisk with a `src` layout and `greenrisk` CLI. +- Add raw-text scoring, explicit climate-gate outcomes, per-score PROV-O, and + strict signal/input validation. +- Separate lightweight, model, research, and development dependencies. +- Pin the TCFD dataset revision and record complete run/environment metadata for + future runs. +- Repair historical artifact hashes for Git-normalized bytes and add automated + manifest/PROV-O integrity verification. +- Add pytest, lint, build CI, and a manual heavyweight model-smoke workflow. +- Clarify responsible-use boundaries and limitations of the validation claims. +- Preserve the `rulebase-locked-v1` numerical instrument unchanged. + +## 0.1.0 — 2026-07-02 + +- Initial archived research release. diff --git a/CITATION.cff b/CITATION.cff index 31bfa4d..12d9e28 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -13,15 +13,11 @@ authors: given-names: Isaac email: isaac.dcesares@ufrj.br affiliation: "Universidade Federal do Rio de Janeiro (UFRJ) — Programa de Pós-Graduação em Informática (PPGI)" -version: 0.1.0 -date-released: "2026-07-02" +version: 0.2.0 url: "https://github.com/idcesares/GreenRisk" repository-code: "https://github.com/idcesares/GreenRisk" license: Apache-2.0 identifiers: - - type: doi - value: "10.5281/zenodo.21122390" - description: "Archived snapshot of this specific software version (v0.1.0) on Zenodo." - type: doi value: "10.5281/zenodo.21122389" description: "Concept DOI representing all versions of this software; always resolves to the latest archived release." diff --git a/MASTER_PLAN.md b/MASTER_PLAN.md index 7a9bfc4..feb040a 100644 --- a/MASTER_PLAN.md +++ b/MASTER_PLAN.md @@ -1,138 +1,117 @@ # GreenRisk — Master Plan -> Canonical map of the repository: what exists, where it lives, and how to -> reproduce every committed artifact. For the pipeline design and the -> reasoning behind it, see [`docs/architecture.md`](docs/architecture.md). -> For the validity evidence, see [`docs/validation.md`](docs/validation.md). -> For the decisions that produced the locked instrument, see -> [`docs/decisions.md`](docs/decisions.md). - -Instrument status: **frozen at tag `rulebase-locked-v1`.** After this freeze, -any change to the rules, membership functions, model revisions, or signal -mappings is a separate, explicitly logged instrument change — never a silent -edit. The validity evidence in `docs/validation.md` was produced against this -exact, unchanging instrument, including a held-out case set that was not -inspected or scored until after the freeze. - -## 1. Locked instrument at a glance - -**Signals** (construct-direct; risk-direction inversion happens in the rule -base, not the signal): - -| Fuzzy input | Model | Label read | -| --- | --- | --- | -| `specificity` | climate-specificity | `P('spec')` | -| `commitment` | climate-commitment | `P('yes')` | -| `sentiment_asymmetry` | climate-sentiment | `P('opportunity')` | -| `netzero` | netzero-reduction | `P('net-zero')` | -| *(gate)* | climate-detector | `P('yes') ≥ 0.5` — relevance gate, not a fuzzy axis | - -**Membership functions** — all triangular: -- inputs on `[0, 1]`: Low `(0, 0, 0.4)` · Medium `(0.2, 0.5, 0.8)` · High `(0.6, 1, 1)` -- output `risk` on `[0, 100]`: Low `(0, 0, 30)` · Moderate `(15, 35, 55)` · Elevated `(45, 65, 85)` · High `(70, 100, 100)` - -**Rules** — 17, `risk = vagueness × claim-strength`: -- Tier 1 spine S1–S9 (specificity × commitment) -- Tier 2 net-zero amplifiers N1–N3 -- Tier 3 opportunity amplifiers O1–O3 -- Tier 4 named signatures G1–G2 (trace legibility for case studies) - -Defuzzification: centroid. The full rationale for every choice above — -including the design alternatives that were tested and not adopted — is in -[`docs/architecture.md`](docs/architecture.md). +This is the canonical repository map and reproducibility contract. The +numerical instrument is frozen at `rulebase-locked-v1` (commit +`a40288ac8eacfe0de1987884ec03bea9457972c5`). Changes to `RULES`, membership +functions, model revisions, or signal mappings require a separately identified +and logged ablation. Packaging, validation, provenance, and documentation may +evolve without altering that instrument. + +## 1. Locked instrument + +| Input | Pinned classifier output | +| --- | --- | +| `specificity` | climate-specificity `P(spec)` | +| `commitment` | climate-commitment `P(yes)` | +| `sentiment_asymmetry` | climate-sentiment `P(opportunity)` | +| `netzero` | netzero-reduction `P(net-zero)` | +| climate gate | climate-detector `P(yes) ≥ 0.5` | + +Inputs use triangular Low `(0,0,0.4)`, Medium `(0.2,0.5,0.8)`, and High +`(0.6,1,1)` membership functions. Output risk uses Low `(0,0,30)`, Moderate +`(15,35,55)`, Elevated `(45,65,85)`, and High `(70,100,100)`, with centroid +defuzzification. The 17 rules comprise spine S1–S9, net-zero N1–N3, +opportunity O1–O3, and named signatures G1–G2. ## 2. Repository map +```text +src/greenrisk/ installable package + metadata.py frozen identifiers, model registry, signal map + models.py optional ClimateBERT inference adapters + linguistic_variables.py membership functions + rule_base.py rules, validation, score and trace + provenance.py per-score PROV-O + cli.py score-signals and score-text commands +scripts/ artifact and research-run producers +scripts/validation/ research validation harnesses +tests/ fast pytest suite and optional model tests +data/ contrast-set source and data documentation +artifacts/ committed results, figures, manifests, PROV-O +docs/ design, evidence, decisions, use policy, paper +.github/workflows/ fast CI and manually triggered model smoke tests ``` -GreenRisk/ -├── MASTER_PLAN.md this file — the repository map -├── README.md -├── LICENSE -├── models.py linguistic_variables.py rule_base.py main.py core instrument modules -├── scripts/ pipeline & artifact producers -│ ├── pin_models.py pin ClimateBERT revisions for reproducibility -│ ├── first_provenance.py minimal PROV-O example → artifacts/provenance/ -│ ├── plot_linguistic_variables.py → artifacts/figures/ (membership-function plots) -│ ├── sanity_check_distributions.py → artifacts/figures/ (signal distributions) -│ ├── run_full_corpus.py, bingler_baseline.py, provenance_corpus_run.py -│ │ → artifacts/corpus_run/ — Layer 1 validation (docs/validation.md) -│ ├── run_contrast_set.py, evaluate_face_validity.py, provenance_contrast_run.py -│ │ → artifacts/contrast_run/ — Layer 2 validation (docs/validation.md) -│ └── validation/ locked-instrument evidence harnesses -│ ├── integration_seam_test.py end-to-end seam test + PROV-O -│ ├── mf_experiment.py triangular vs. trapezoidal membership-function comparison -│ ├── anchor_verify.py calibration-anchor verification -│ └── hash3_characterize.py sizes the commitment-classifier false-positive tail -├── tests/ smoke tests (models, detector, provenance) + -│ property_test_rule_base.py (locked-instrument -│ properties; no model download required) -├── data/ contrast_set.csv — the held-out Layer 2 case set -├── artifacts/ figures/ (plots) · provenance/ (.ttl/.json/.png) · corpus_run/ · contrast_run/ -├── docs/ greenrisk_paper.pdf · architecture.md · validation.md · -│ decisions.md · ai-usage.md · acknowledgements.md -└── development/ internal working record (design notes, decision log); - gitignored, not part of the public release + +## 3. Environments + +```bash +# Base fuzzy scorer + tests (no model weights) +uv sync --frozen --group dev +uv run pytest -m "not model" +uv run python scripts/verify_artifact_integrity.py +uv build + +# Raw-text model inference +uv sync --frozen --extra models --group dev +uv run pytest -m model +uv run python scripts/verify_model_revisions.py + +# Full artifact reproduction +uv sync --frozen --extra research --group dev ``` -## 3. Reproducibility +The model smoke workflow is manual because it downloads several large model +artifacts. The normal CI job remains fast and checks packaging, lint, rule-base +properties, API validation, provenance hashes, and wheel/sdist creation. -```powershell -# pin + verify models (writes pinned_model_hashes.md, gitignored) -uv run python scripts/pin_models.py -uv run python tests/smoke_test_all_models.py +## 4. Artifact reproduction -# regenerate committed artifacts +```bash uv run python scripts/plot_linguistic_variables.py uv run python scripts/first_provenance.py -# re-run the locked-instrument evidence uv run python scripts/validation/integration_seam_test.py -n 20 --gate 0.5 uv run python scripts/validation/mf_experiment.py -n 200 --gate 0.5 uv run python scripts/validation/anchor_verify.py uv run python scripts/validation/hash3_characterize.py -n 500 --gate 0.5 -# Layer 1 — full-corpus run + baseline comparison + run-level provenance uv run python scripts/run_full_corpus.py --gate 0.5 uv run python scripts/bingler_baseline.py uv run python scripts/provenance_corpus_run.py -# Layer 2 — held-out face validity (contrast set) + provenance uv run python scripts/run_contrast_set.py --gate 0.5 uv run python scripts/evaluate_face_validity.py uv run python scripts/provenance_contrast_run.py - -# smoke tests -uv run python tests/smoke_test_all_models.py -uv run python tests/smoke_test_detector_model.py -uv run python tests/smoke_test_provenance.py - -# locked rule-base properties (no model download; runs in seconds) -uv run python tests/property_test_rule_base.py +uv run python scripts/verify_artifact_integrity.py ``` -All corpus-scale runs use `climatebert/tcfd_recommendations` (TCFD only). The -contrast set (`data/contrast_set.csv`) is reserved for the one-shot, -held-out Layer 2 evaluation described in `docs/validation.md`. - -Model inference runs on GPU automatically if a CUDA-capable device is -available (`torch.cuda.is_available()`), and falls back to CPU otherwise — see -`models.py`. No GPU is required to run the pipeline; it is only faster with -one. - -## 4. Conventions - -- **Production vs. process split.** Tracked: core modules, `scripts/`, - `tests/`, `data/`, `artifacts/`, `docs/`, this file. Gitignored: - `development/` (design notes, decision log, internal working material). -- **The decision log is append-only.** Every instrument change gets a - logged entry before it is relied on, in `development/decisions/` (internal - working log). The public record of those decisions — the `DL-00x` entries - cited by the paper, the lock, and the blind pre-registration of the held-out - test — is [`docs/decisions.md`](docs/decisions.md), with the resulting - rationale written up in `docs/architecture.md` and `docs/validation.md`. -- **Claims about the rule base are tested, not just written.** The properties - stated in `docs/architecture.md` §5 and `docs/decisions.md` (DL-001) are - asserted in `tests/property_test_rule_base.py`, so a change that breaks them - fails loudly. -- **Models are pinned** by commit revision in `models.MODEL_REGISTRY` for - bit-identical reproducibility; PROV-O graphs record the resulting hashes. +The TCFD dataset is pinned at revision +`aee5c98f0bf7835bfb08308ffc7c17216657976b`; the train Parquet content SHA-256 +is `1f16bb38a021aeafad6935eb5aa3f072c2e51b3dcf01eaee88a32ef537029f1f`. +Manifests record exact model revisions, input/output hashes, the instrument +commit, the lockfile hash, and runtime versions for new runs. + +Historical corpus and contrast manifests were expanded using immutable Git and +Hugging Face records. They explicitly mark original runtime details that were +not recorded. Their content hashes use Git's normalized LF bytes, enforced by +`.gitattributes` and verified in CI. + +The current producers intentionally retain full input hashes and unrounded +signal/trace values, so their serialized CSV bytes differ from the historical +runs even when numerical scores agree. For byte-identical historical outputs, +check out the `source.commit` in the relevant manifest and use its recorded +lockfile. A current-code rerun is a new run and must keep its new manifest and +PROV-O hashes together. + +## 5. Governance + +- `docs/decisions.md` is append-only for instrument and evidence decisions. +- Research outputs are immutable evidence. Regenerate them only with the + producing script and update the manifest and PROV-O records together. +- Never silently float model or dataset revisions. +- Never persist credentials during imports; `HF_TOKEN` is passed only to model + download requests when present. +- Scores must retain raw-probability precision in machine-readable output; + rounding is presentation-only except for the locked 2-decimal risk result. +- Claims must distinguish transparent fuzzy reasoning from learned upstream + classifiers and exploratory characterization from independent validation. diff --git a/README.md b/README.md index 08b9218..47713c2 100644 --- a/README.md +++ b/README.md @@ -1,116 +1,118 @@ # GreenRisk +[![CI](https://github.com/idcesares/GreenRisk/actions/workflows/ci.yml/badge.svg)](https://github.com/idcesares/GreenRisk/actions/workflows/ci.yml) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21122389.svg)](https://doi.org/10.5281/zenodo.21122389) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE) -Explainable greenwashing-risk scoring for corporate climate disclosures: -pinned ClimateBERT signals → a Mamdani fuzzy rule base → an auditable 0–100 risk -score with a W3C PROV-O provenance graph. - -Every score ships with an activation trace showing exactly which rules fired -and why, and a provenance graph binding the result to the exact model -revisions and instrument version that produced it. Nothing is a black box. +GreenRisk is a research instrument for screening greenwashing risk in corporate +climate-disclosure paragraphs. It combines pinned ClimateBERT classifiers with +a frozen 17-rule Mamdani fuzzy layer and returns a 0–100 score plus the rules +that fired. W3C PROV-O records bind scores and committed evaluations to model, +dataset, instrument, and artifact identifiers. + +The fuzzy decision layer is transparent and independently traceable. The +upstream neural classifiers remain learned models and should not be described +as intrinsically interpretable. The numerical instrument is frozen at +`rulebase-locked-v1` (commit `a40288a`); release 0.2.0 hardens the surrounding +software without changing its membership functions, rules, model revisions, or +signal mappings. + +## Use boundaries + +GreenRisk is for research, prioritization, and decision support. It is not a +legal finding, assurance opinion, fact checker, or substitute for reviewing the +underlying disclosure and evidence. Scores are construct-dependent, inherit +classifier error, and do not cover all greenwashing forms—especially omission +or misleading claims that are linguistically specific. See +[`docs/responsible-use.md`](docs/responsible-use.md). + +## Install and score + +Install the fast fuzzy layer and development checks: + +```bash +uv sync --frozen --group dev +uv run greenrisk score-signals \ + --specificity 0.20 \ + --commitment 0.90 \ + --sentiment-asymmetry 0.40 \ + --netzero 0.70 +``` -Instrument status: frozen at tag `rulebase-locked-v1`. +Raw-text scoring additionally needs the pinned ClimateBERT models: -## Paper +```bash +uv sync --frozen --extra models +uv run greenrisk score-text --text "We aim to reach net zero by 2050." --json +``` -**GreenRisk: An Explainable-by-Construction Fuzzy Instrument for Scoring -Greenwashing Risk in Corporate Climate Disclosures.** Isaac D'Césares de -Carvalho Lima, Maria Luiza Machado Campos, and Sérgio Manuel Serra da Cruz. -Programa de Pós-Graduação em Informática (PPGI), Universidade Federal do Rio de -Janeiro, 2026. → [`docs/greenrisk_paper.pdf`](docs/greenrisk_paper.pdf) +Use `--provenance score.ttl` on `score-text` to write a per-score PROV-O graph. +Text below the climate-relevance gate is explicitly returned as `not_scored`; +`--force` overrides the gate and records that fact. Model downloads are large, +may require network access, and use CUDA automatically when available. + +## Evidence and limitations + +The paper reports two exploratory validation layers: + +- A comparison on 1,009 climate-gated TCFD paragraphs against a specificity- + and commitment-derived cheap-talk baseline (Spearman ρ = 0.602). This is + construct-alignment evidence, not independent criterion validation, because + GreenRisk and the baseline share classifier-derived constructs. +- A 15-case post-lock contrast evaluation, with AUC = 0.867 for the primary + 5-vs-6 subset. The expectations were not used to tune the frozen instrument, + but they and the first results entered public history in the same commit. The + repository therefore does not claim an externally timestamped blind + preregistration. + +These results are small-sample, paragraph-level characterization—not a claim of +population-level or organization-level validity. Full interpretation is in +[`docs/validation.md`](docs/validation.md). + +## Reproduce and verify + +```bash +uv run pytest -m "not model" +uv run python scripts/verify_artifact_integrity.py +uv build + +# Optional heavyweight model checks +uv sync --frozen --extra models --group dev +uv run pytest -m model + +# Full research environment and artifact producers +uv sync --frozen --extra research --group dev +uv run python scripts/run_full_corpus.py --gate 0.5 +uv run python scripts/provenance_corpus_run.py +uv run python scripts/run_contrast_set.py --gate 0.5 +uv run python scripts/evaluate_face_validity.py +uv run python scripts/provenance_contrast_run.py +``` -The paper reports the instrument's design and its two-layer evaluation: a -convergent/discriminant comparison against the Bingler et al. cheap-talk -baseline on 1,009 climate-gated TCFD paragraphs (Spearman ρ = 0.602; ρ = 0.433 -between risk and commitment inside the baseline's vaguest quartile), and a -held-out, one-shot test on 15 regulator-adjudicated and third-party-verified -cases (AUC = 0.867, n = 5 vs. 6). Every figure it reports is reproducible from -this repository — see [`MASTER_PLAN.md`](MASTER_PLAN.md) for the commands and -[`docs/decisions.md`](docs/decisions.md) for the `DL-00x` decisions it cites. +The committed historical manifests were expanded from immutable Git and +Hugging Face history. Details unavailable from the original runtime are marked +as unavailable rather than reconstructed speculatively. Dataset and artifact +hashes are checked in CI. See [`MASTER_PLAN.md`](MASTER_PLAN.md) for the complete +repository map and reproduction contract. Current producers use hardened, +higher-precision serialization; byte-identical historical reproduction starts +from each manifest's `source.commit`, while current-code reruns test numerical +equivalence and produce new hashes. ## Documentation -- [`docs/greenrisk_paper.pdf`](docs/greenrisk_paper.pdf) — the final project - paper (see [Paper](#paper) above). -- [`docs/architecture.md`](docs/architecture.md) — the full pipeline: the - climate-relevance gate, the four signals, the fuzzy rule base, and the - design rationale behind it. -- [`docs/validation.md`](docs/validation.md) — the validity evidence: a - large-scale statistical comparison against a published baseline, and a - held-out test against real, regulator-adjudicated cases, including the - instrument's documented scope limitation. -- [`docs/decisions.md`](docs/decisions.md) — the decision record: every - instrument decision with the evidence behind it, the alternatives rejected, - the lock, and the blind pre-registration of the held-out test. -- [`docs/ai-usage.md`](docs/ai-usage.md) — how generative AI was and was not - used in building this artifact, and what makes that division checkable. -- [`docs/acknowledgements.md`](docs/acknowledgements.md) — citations for the - ClimateBERT models, datasets, and standards this project builds on. -- [`MASTER_PLAN.md`](MASTER_PLAN.md) — the repository map, the locked - instrument at a glance, and exact reproduction commands. - -## Repository Map - -- `models.py`, `linguistic_variables.py`, `rule_base.py` — locked scoring - instrument. -- `main.py` — small CLI for scoring already-computed signal probabilities. -- `scripts/` — artifact producers for figures, full-corpus runs, contrast-set - runs, baselines, and provenance. -- `scripts/validation/` — validation harnesses for anchors, hash behavior, and - integrated scoring. -- `data/contrast_set.csv` — held-out contrast set used after the lock. -- `artifacts/` — committed outputs used by the validity argument. -- `tests/` — smoke tests, plus `property_test_rule_base.py`, which checks the - locked rule base's stated properties without downloading any model. -- `docs/` — public documentation, the decision record, and the project paper. - -## Quick Start - -```powershell -uv sync -uv run python main.py --specificity 0.20 --commitment 0.90 --sentiment-asymmetry 0.40 --netzero 0.70 -uv run python scripts/validation/integration_seam_test.py -n 20 --gate 0.5 -``` +- [`docs/architecture.md`](docs/architecture.md) — pipeline and design rationale +- [`docs/validation.md`](docs/validation.md) — evidence, protocol, and limitations +- [`docs/decisions.md`](docs/decisions.md) — public decision record and freeze +- [`docs/responsible-use.md`](docs/responsible-use.md) — intended and prohibited uses +- [`docs/ai-usage.md`](docs/ai-usage.md) — AI-assistance disclosure +- [`docs/acknowledgements.md`](docs/acknowledgements.md) — upstream work and models +- [`docs/greenrisk_paper.pdf`](docs/greenrisk_paper.pdf) — project paper + +## Citation and license + +Use [`CITATION.cff`](CITATION.cff) or the Zenodo concept DOI +[`10.5281/zenodo.21122389`](https://doi.org/10.5281/zenodo.21122389). A new +version-specific DOI should be minted when 0.2.0 is released; the existing +`10.5281/zenodo.21122390` identifies v0.1.0. -The CLI path does not load ClimateBERT; it scores four precomputed signal values -through the locked Mamdani rule base and prints the rule-activation trace. Scripts -that call `models.py` may download/load the pinned Hugging Face model revisions on -first run. Inference uses a GPU automatically if one is available and falls back to -CPU otherwise — a GPU is not required. - -## Citation - -If you use GreenRisk, cite the paper: - -> Isaac D'Césares de Carvalho Lima, Maria Luiza Machado Campos, and Sérgio -> Manuel Serra da Cruz. *GreenRisk: An Explainable-by-Construction Fuzzy -> Instrument for Scoring Greenwashing Risk in Corporate Climate Disclosures.* -> Programa de Pós-Graduação em Informática (PPGI), Universidade Federal do Rio -> de Janeiro, 2026. - -To cite the software itself, use [`CITATION.cff`](CITATION.cff) -(GitHub renders a "Cite this repository" button from this file), or its -Zenodo archive: **10.5281/zenodo.21122389** — this concept DOI always -resolves to the latest release; see the -[Zenodo record](https://doi.org/10.5281/zenodo.21122389) for version-specific -DOIs (e.g. v0.1.0 is `10.5281/zenodo.21122390`). See -[`docs/acknowledgements.md`](docs/acknowledgements.md) for the papers and -models this project is built on. - -## AI Usage - -Generative-AI tools were used as an execution and review layer in building this -software — implementation, debugging, testing, and reproducibility engineering — -under author direction and with author review. The instrument's design, its rule -base, the evaluation protocol, and the interpretation of the evidence were -determined by the authors, and each decision is logged with its evidence in -[`docs/decisions.md`](docs/decisions.md). See -[`docs/ai-usage.md`](docs/ai-usage.md) for the full statement. - -## License - -Apache License 2.0 — see [`LICENSE`](LICENSE). The underlying ClimateBERT -models are also Apache 2.0 licensed; see -[`docs/acknowledgements.md`](docs/acknowledgements.md) for full citations. +Apache License 2.0. Upstream model and dataset terms remain applicable. diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 0000000..66a3f69 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,15 @@ +# Security policy + +GreenRisk is research software and has no hosted service maintained by this +repository. For vulnerabilities that could expose credentials, execute code, +or compromise users, use GitHub private vulnerability reporting when available. +Do not publish secrets, private disclosures, or exploit payloads in an issue. + +Scientific validity concerns, unexpected scores, and documentation errors are +not security vulnerabilities. Report them in a public issue with the software +version, instrument tag, input hashes or non-sensitive examples, command used, +and relevant trace/provenance output. + +Only the latest repository release is supported. Model and dataset downloads +come from pinned Hugging Face revisions, but users remain responsible for +reviewing upstream artifacts and licenses in their deployment environment. diff --git a/artifacts/contrast_run/run_manifest.json b/artifacts/contrast_run/run_manifest.json index 95de40f..195071e 100644 --- a/artifacts/contrast_run/run_manifest.json +++ b/artifacts/contrast_run/run_manifest.json @@ -1,17 +1,47 @@ { + "schema_version": "2.0", + "software_version": "0.2.0-research", + "source": { + "commit": "8b2f4615c1ab653000fc5413bac23d83c422098a", + "dirty": "not recorded by the legacy runner" + }, "instrument_tag": "rulebase-locked-v1", - "dataset": "data/contrast_set.csv (held-out)", + "instrument_commit": "a40288ac8eacfe0de1987884ec03bea9457972c5", + "dataset": "data/contrast_set.csv", + "dataset_sha256": "1ce0dcea54fba8fb49c2c8827e93280098ff5e286e41800e784fcfeb383e30c6", "gate": 0.5, "batch_size": 32, "n": 15, "n_gate_pass": 15, - "prereg_ratified": "2026-06-17", - 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b/artifacts/provenance/contrast_run.ttl @@ -1,4 +1,4 @@ -@prefix gr: . +@prefix gr: . @prefix prov: . @prefix xsd: . @@ -8,7 +8,7 @@ gr:contrast_scored_csv a prov:Entity, gr:instrument_rulebase-locked-v1 ; prov:wasGeneratedBy gr:contrast_run ; gr:filename "contrast_scored.csv"^^xsd:string ; - gr:sha256 "17c69b77a2ca42ea13450e24a4880a04ac8d620cd886f69d01ecec7d7f92cf59"^^xsd:string . + gr:sha256 "8ddcb06b0950150807df1cc1c359bf4f47027c9e7f41bc74e5078a1f91c69bb0"^^xsd:string . gr:face_validity_stats_json a prov:Entity, "gr:FaceValidityStats"^^xsd:string ; @@ -16,11 +16,11 @@ gr:face_validity_stats_json a prov:Entity, gr:instrument_rulebase-locked-v1 ; prov:wasGeneratedBy gr:contrast_run ; gr:filename "face_validity_stats.json"^^xsd:string ; - gr:sha256 "043e9d1973b02bde4dce6c781ecb5b4dfb505bd3f70015ea48f6f18391e9368e"^^xsd:string . + gr:sha256 "30539bcaf8d72cba60d21416324beb2fc2c1ed55b2d3e965e16e4e5d25cceb31"^^xsd:string . -gr:GreenRiskPipeline_v0.2 a prov:Agent, +gr:GreenRiskPipeline_v0.2.0-research a prov:Agent, "prov:SoftwareAgent"^^xsd:string ; - gr:version "0.2.0"^^xsd:string . + gr:version "0.2.0-research"^^xsd:string . a prov:Entity, "gr:PretrainedModel"^^xsd:string ; @@ -58,19 +58,22 @@ gr:contrast_run a prov:Activity, , , ; - prov:wasAssociatedWith gr:GreenRiskPipeline_v0.2 ; + prov:wasAssociatedWith gr:GreenRiskPipeline_v0.2.0-research ; gr:gate "0.5"^^xsd:string ; gr:n "15"^^xsd:string ; gr:n_gate_pass "15"^^xsd:string . gr:contrast_set a prov:Entity, "gr:HeldOutContrastSet"^^xsd:string ; + gr:expectations_recorded "2026-06-17"^^xsd:string ; gr:n "15"^^xsd:string ; - gr:name "data/contrast_set.csv (held-out)"^^xsd:string ; - gr:prereg_ratified "2026-06-17"^^xsd:string . + gr:name "data/contrast_set.csv"^^xsd:string ; + gr:protocol_status "expectations recorded in the same public commit as first results; not an externally timestamped preregistration"^^xsd:string ; + gr:sha256 "1ce0dcea54fba8fb49c2c8827e93280098ff5e286e41800e784fcfeb383e30c6"^^xsd:string . gr:instrument_rulebase-locked-v1 a prov:Entity, "gr:FuzzyInstrument"^^xsd:string ; + gr:commit "a40288ac8eacfe0de1987884ec03bea9457972c5"^^xsd:string ; gr:defuzz "Mamdani/centroid"^^xsd:string ; gr:n_rules "17"^^xsd:string ; gr:tag "rulebase-locked-v1"^^xsd:string . diff --git a/artifacts/provenance/corpus_run.json b/artifacts/provenance/corpus_run.json index 3357f46..f5af759 100644 --- a/artifacts/provenance/corpus_run.json +++ b/artifacts/provenance/corpus_run.json @@ -1 +1 @@ -{"prefix": {"gr": "https://greenrisk.ppgi.ufrj.br/prov/", "hf": "https://huggingface.co/", "default": "https://greenrisk.ppgi.ufrj.br/prov/"}, "agent": {"gr:GreenRiskPipeline_v0.2": {"prov:type": "prov:SoftwareAgent", "gr:version": "0.2.0"}}, "entity": {"gr:instrument_rulebase-locked-v1": {"prov:type": "gr:FuzzyInstrument", "gr:tag": "rulebase-locked-v1", "gr:n_rules": "17", "gr:defuzz": "Mamdani/centroid"}, "gr:dataset_tcfd": {"prov:type": "gr:Corpus", "gr:name": "climatebert/tcfd_recommendations", "gr:n_total": "1300"}, "hf:climatebert/distilroberta-base-climate-detector": {"prov:type": "gr:PretrainedModel", "gr:short_name": "detector", "gr:commit_hash": "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c"}, "hf:climatebert/distilroberta-base-climate-specificity": {"prov:type": "gr:PretrainedModel", "gr:short_name": "specificity", "gr:commit_hash": "4ada96ed4bf5c3a7a711282e41f1ab9b29f0ddea"}, "hf:climatebert/distilroberta-base-climate-commitment": {"prov:type": "gr:PretrainedModel", "gr:short_name": "commitment", "gr:commit_hash": "17337c3292df16a8fe93b1505dfe4122d50a4c91"}, "hf:climatebert/distilroberta-base-climate-sentiment": {"prov:type": "gr:PretrainedModel", "gr:short_name": "sentiment", "gr:commit_hash": "e9f9a94ee4263f5ad5cfc97b8539a497fc88aa7d"}, "hf:climatebert/netzero-reduction": {"prov:type": "gr:PretrainedModel", "gr:short_name": "netzero", "gr:commit_hash": "25cf57e30613a2156fee1fe3f917036df4a5c0d1"}, 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gr:corpus_run a prov:Activity, , , ; - prov:wasAssociatedWith gr:GreenRiskPipeline_v0.2 ; + prov:wasAssociatedWith gr:GreenRiskPipeline_v0.2.0-research ; gr:batch_size "32"^^xsd:string ; gr:gate "0.5"^^xsd:string ; gr:n_kept "1009"^^xsd:string ; @@ -60,10 +60,13 @@ gr:corpus_run a prov:Activity, gr:dataset_tcfd a prov:Entity, "gr:Corpus"^^xsd:string ; gr:n_total "1300"^^xsd:string ; - gr:name "climatebert/tcfd_recommendations"^^xsd:string . + gr:name "climatebert/tcfd_recommendations"^^xsd:string ; + gr:revision "aee5c98f0bf7835bfb08308ffc7c17216657976b"^^xsd:string ; + gr:sha256 "1f16bb38a021aeafad6935eb5aa3f072c2e51b3dcf01eaee88a32ef537029f1f"^^xsd:string . gr:instrument_rulebase-locked-v1 a prov:Entity, "gr:FuzzyInstrument"^^xsd:string ; + gr:commit "a40288ac8eacfe0de1987884ec03bea9457972c5"^^xsd:string ; gr:defuzz "Mamdani/centroid"^^xsd:string ; gr:n_rules "17"^^xsd:string ; gr:tag "rulebase-locked-v1"^^xsd:string . diff --git a/artifacts/provenance/first_provenance.json 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a/artifacts/provenance/first_provenance.ttl b/artifacts/provenance/first_provenance.ttl index 939813d..9861f9a 100644 --- a/artifacts/provenance/first_provenance.ttl +++ b/artifacts/provenance/first_provenance.ttl @@ -1,26 +1,26 @@ -@prefix gr: . +@prefix gr: . @prefix prov: . @prefix xsd: . -gr:score_5441f710bb76_distilroberta-base-climate-detector a prov:Entity, +gr:score_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114_distilroberta-base-climate-detector a prov:Entity, "gr:ClassificationScore"^^xsd:string ; - prov:wasDerivedFrom gr:paragraph_5441f710bb76 ; - prov:wasGeneratedBy gr:inference_5441f710bb76_distilroberta-base-climate-detector ; + prov:wasDerivedFrom gr:paragraph_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114 ; + prov:wasGeneratedBy gr:inference_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114_distilroberta-base-climate-detector ; gr:label "yes"^^xsd:string ; gr:model_used "climatebert/distilroberta-base-climate-detector"^^xsd:string ; gr:probability "0.99"^^xsd:string . -gr:GreenRiskPipeline_v0.1 a prov:Agent, +gr:GreenRiskPipeline_v0.2.0 a prov:Agent, "prov:SoftwareAgent"^^xsd:string ; - gr:version "0.1.0"^^xsd:string . + gr:version "0.2.0"^^xsd:string . -gr:inference_5441f710bb76_distilroberta-base-climate-detector a prov:Activity, +gr:inference_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114_distilroberta-base-climate-detector a prov:Activity, "gr:ModelInference"^^xsd:string ; - prov:endedAtTime "2026-05-05T23:12:22.617380+00:00"^^xsd:dateTime ; - prov:startedAtTime "2026-05-05T23:12:22.617380+00:00"^^xsd:dateTime ; - prov:used gr:paragraph_5441f710bb76, + prov:endedAtTime "2026-09-04T17:40:51.729144+00:00"^^xsd:dateTime ; + prov:startedAtTime "2026-09-04T17:40:51.729144+00:00"^^xsd:dateTime ; + prov:used gr:paragraph_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114, ; - prov:wasAssociatedWith gr:GreenRiskPipeline_v0.1 ; + prov:wasAssociatedWith gr:GreenRiskPipeline_v0.2.0 ; gr:max_length "512"^^xsd:string ; gr:model_commit "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c"^^xsd:string ; gr:model_repo "climatebert/distilroberta-base-climate-detector"^^xsd:string ; @@ -31,9 +31,9 @@ gr:inference_5441f710bb76_distilroberta-base-climate-detector a prov:Activity, gr:commit_hash "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c"^^xsd:string ; gr:huggingface_repo "climatebert/distilroberta-base-climate-detector"^^xsd:string . -gr:paragraph_5441f710bb76 a prov:Entity, +gr:paragraph_5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114 a prov:Entity, "gr:InputParagraph"^^xsd:string ; gr:char_count "272"^^xsd:int ; gr:text_preview "We are committed to achieving net-zero greenhouse gas emissions across our entire value chain by 205..."^^xsd:string ; - gr:text_sha256 "5441f710bb76"^^xsd:string . + gr:text_sha256 "5441f710bb76a223fd7dc04bc884035c1310c6d45824062e4ce0c1d7cc55e114"^^xsd:string . diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..4704ae6 --- /dev/null +++ b/data/README.md @@ -0,0 +1,16 @@ +# Data + +`contrast_set.csv` contains 15 short excerpts and source metadata used in the +post-lock contrast evaluation. The file SHA-256 is recorded in +`artifacts/contrast_run/run_manifest.json` and verified in CI. + +The TCFD corpus is not vendored. Research scripts load +`climatebert/tcfd_recommendations` at revision +`aee5c98f0bf7835bfb08308ffc7c17216657976b`; the train Parquet LFS object has +SHA-256 `1f16bb38a021aeafad6935eb5aa3f072c2e51b3dcf01eaee88a32ef537029f1f`. +Consult the upstream dataset card for provenance, licensing, and limitations. + +Contrast excerpts remain subject to their underlying source terms. Their +inclusion supports research reproducibility and does not imply endorsement or +a new adjudication by the GreenRisk authors. Do not use this small set as a +general benchmark or split it further for model fitting. diff --git a/docs/README.md b/docs/README.md index ab78eed..e37d8c6 100644 --- a/docs/README.md +++ b/docs/README.md @@ -7,16 +7,15 @@ climate-relevance gate, the four ClimateBERT signals, why the project uses fuzzy inference, the membership functions, and the 17-rule Mamdani base, worked through with examples. -- [`validation.md`](validation.md) — the validity evidence, in two - independent layers: a 1,009-paragraph statistical comparison against a - published baseline, and a held-out, case-level test against real, - regulator-adjudicated companies. Includes the instrument's documented scope - limitation. +- [`validation.md`](validation.md) — two exploratory evidence layers: a + 1,009-paragraph construct comparison and a 15-case post-lock contrast + evaluation, with their chronology and independence limits. - [`decisions.md`](decisions.md) — the decision record: every instrument decision in the order it was taken, with the evidence behind it, the - alternatives that were rejected, the lock itself, and the blind - pre-registration of the held-out test. The `DL-00x` identifiers cited in the - paper resolve here. + alternatives that were rejected, the lock itself, and the corrected public + chronology of the contrast evaluation. The `DL-00x` identifiers resolve here. +- [`responsible-use.md`](responsible-use.md) — intended uses, prohibited uses, + known limitations, and a review checklist. - [`ai-usage.md`](ai-usage.md) — how generative AI was and was not used in building this artifact, and what makes that division checkable. - [`acknowledgements.md`](acknowledgements.md) — citations for the ClimateBERT diff --git a/docs/acknowledgements.md b/docs/acknowledgements.md index c7b8dc5..a946d4e 100644 --- a/docs/acknowledgements.md +++ b/docs/acknowledgements.md @@ -9,7 +9,7 @@ All four signal classifiers are pinned, unmodified checkpoints from the [ClimateBERT](https://huggingface.co/climatebert) family, hosted on the Hugging Face Hub under the `climatebert` organization and released under the Apache License 2.0. GreenRisk loads them at fixed revisions (see -[`models.py`](../models.py)) and does not fine-tune or otherwise alter their +[`metadata.py`](../src/greenrisk/metadata.py)) and does not fine-tune or otherwise alter their weights. **Base language model** diff --git a/docs/ai-usage.md b/docs/ai-usage.md index 549e4cb..ce3068c 100644 --- a/docs/ai-usage.md +++ b/docs/ai-usage.md @@ -36,9 +36,8 @@ judgments: constructs; - the signal mappings, the membership functions, and the 17-rule Mamdani base, including the DL-001 correction that reshaped the spine; -- the evaluation protocol — the decision to freeze the instrument, the blind - pre-registration of the held-out contrast set, and the one-shot scoring - discipline; +- the evaluation protocol — the decision to freeze the instrument, record + expectations for the contrast set, and avoid post-lock tuning; - the interpretation of the evidence, the scope limitation the results expose, and the conclusions drawn from them. @@ -55,11 +54,10 @@ rather than a claim about intent: - every instrument decision was logged **before** it was relied on, and the alternatives that were rejected are logged with it; - the instrument was frozen at `rulebase-locked-v1` (commit `a40288a`) before - the held-out case set was inspected, so no post-hoc adjustment — by a human or - a model — could have fitted it to the test; -- the held-out set was scored **once**, against the pre-registered - stratification published in - [Appendix A of the decision record](decisions.md#appendix-a--phase-6-pre-registration-ratified-blind); + the contrast evaluation and was not adjusted afterward; +- the expectations and results are preserved, while the public record also + makes clear that they entered Git history in the same commit and therefore + are not independently verifiable as a blind preregistration; - the five ClimateBERT classifiers are pinned by revision, and every run emits a W3C PROV-O graph binding its results to those revisions and to the instrument tag; diff --git a/docs/architecture.md b/docs/architecture.md index a6aa58a..d2d853f 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -9,8 +9,9 @@ pieces fit together. It assumes no prior context beyond the [README](../README.m GreenRisk reads a single paragraph of corporate climate disclosure and returns an **explainable greenwashing-risk score from 0 to 100**, together with: -- an **activation trace** — the exact set of rules that fired and how strongly, - so the score is re-derivable by inspection rather than trusted as a black box; +- an **activation trace** — the exact fuzzy rules that fired and how strongly, + so the decision layer is re-derivable even though the upstream classifiers + remain learned models; - a **W3C PROV-O provenance graph** — a machine-readable record of which model revisions, inputs, and instrument version produced that score. @@ -30,16 +31,18 @@ raw paragraph ▼ (parallel) provenance PROV-O graph of the whole run (scripts/provenance_*.py) ``` -Core modules, at the repository root: +Core modules, in `src/greenrisk/`: -- [`models.py`](../models.py) — the pinned model registry, the scoring layer, +- [`models.py`](../src/greenrisk/models.py) — the model scoring layer + and climate-relevance gate. Frozen coordinates live in + [`metadata.py`](../src/greenrisk/metadata.py). the signal-to-label mapping (`SIGNAL_MAP`), and the climate-relevance gate. -- [`linguistic_variables.py`](../linguistic_variables.py) — the fuzzy +- [`linguistic_variables.py`](../src/greenrisk/linguistic_variables.py) — the fuzzy variables and their membership functions. -- [`rule_base.py`](../rule_base.py) — the 17-rule Mamdani base, +- [`rule_base.py`](../src/greenrisk/rule_base.py) — the 17-rule Mamdani base, `score_paragraph`, and the audit trace. -- [`main.py`](../main.py) — a CLI that scores four precomputed signal values - through the locked rule base without loading any language model. +- [`cli.py`](../src/greenrisk/cli.py) — CLI paths for dependency-light signal + scoring and optional raw-text model inference. ## 2. Stage 1 — the climate-relevance gate @@ -47,7 +50,7 @@ Not every paragraph in a disclosure document is about climate at all. Before any of the four risk signals run, a fifth ClimateBERT classifier (`climatebert/distilroberta-base-climate-detector`) answers a binary question: is this paragraph climate-relevant? The gate is `P('yes') ≥ 0.5`; paragraphs -that fail it are dropped before they reach the fuzzy layer. `rule_base.py` +that fail it are dropped before they reach the fuzzy layer. `greenrisk.rule_base` assumes this gating has already happened upstream — it only scores paragraphs that are already known to be climate-relevant. @@ -124,7 +127,7 @@ single-point peak — was evaluated against the same 162-paragraph validation sample and produced identical per-band counts and zero band-boundary crossings; the simpler triangular form is used everywhere ([DL-002](decisions.md#dl-002--membership-function-shape-triangular-kept)). The trapezoidal -variant remains in `linguistic_variables.py` (`specificity_trap`) as a +variant remains in `src/greenrisk/linguistic_variables.py` (`specificity_trap`) as a documented, reproducible comparison point, not as part of the scoring path. Rule combination uses **AND = min**; the final score is obtained by **centroid @@ -203,7 +206,7 @@ Two short paragraphs illustrate why the spine is shaped this way: An earlier version of the rule base scored both examples as maximal risk, because it treated "vague" and "greenwashing" as synonyms without checking whether a claim was actually being made. The spine above is the corrected -version, and is the one implemented in `rule_base.py`. The correction, the +version, and is the one implemented in `src/greenrisk/rule_base.py`. The correction, the evidence that forced it, and the five spine cells it changed are recorded as [DL-001](decisions.md#dl-001--demoting-the-absence-corner) in the decision record. @@ -239,7 +242,7 @@ membership functions, model revisions, or signal mappings is treated as a separate, explicitly logged instrument change — never a silent edit. This matters because the validation evidence in [`docs/validation.md`](validation.md) was collected against this exact, unchanging instrument, including a -held-out test set that was not read or scored until after the freeze. Every +post-lock contrast evaluation that was not used to tune the instrument. Every decision that went into the locked state, and the evidence behind each one, is recorded in [`docs/decisions.md`](decisions.md). See [`MASTER_PLAN.md`](../MASTER_PLAN.md) for the repository map and reproduction diff --git a/docs/decisions.md b/docs/decisions.md index 75c1d96..eba52c9 100644 --- a/docs/decisions.md +++ b/docs/decisions.md @@ -11,12 +11,14 @@ that shaped it. Entries are reproduced from the project's append-only working log; the working log itself carries additional internal process material and is not part of the released repository. -Two properties hold across the whole record: +Two properties are supported by the repository history: -- **Everything before the lock was decided on TCFD evidence only.** The - held-out contrast set stayed sealed until after `rulebase-locked-v1`. -- **Nothing after the lock changed the instrument.** DL-004 and DL-005 are - analyses against a frozen artifact, not edits to it. +- **The numerical instrument was frozen before the contrast evaluation.** No + post-lock commit changed its rules, membership functions, model revisions, + or signal mappings. +- **The public record does not independently prove blind preregistration.** The + Phase-6 expectations and first results entered Git history together. DL-006 + records this chronology correction without rewriting the historical entry. | Entry | Date | Subject | Instrument changed? | | --- | --- | --- | --- | @@ -27,6 +29,7 @@ Two properties hold across the whole record: | [LOCK](#lock--rulebase-locked-v1) | 2026-06-16 | Instrument frozen | Frozen | | [DL-004](#dl-004--convergent-and-discriminant-validity-vs-the-cheap-talk-baseline) | 2026-06-16 | Layer 1 corpus validity | No (analysis only) | | [DL-005](#dl-005--held-out-face-validity-and-the-symmetric-specificity-limitation) | 2026-06-17 | Layer 2 held-out validity | No (analysis only) | +| [DL-006](#dl-006--repository-hardening-and-claim-boundaries) | 2026-09-04 | Reproducibility and claim audit | No (software only) | --- @@ -220,7 +223,7 @@ changes a rule's firing only when specificity is the *smallest* antecedent decision-relevant — vagueness is typically co-bounded by low commitment, which caps the minimum regardless of the specificity MF shape. -`specificity_trap` remains in [`linguistic_variables.py`](../linguistic_variables.py) +`specificity_trap` remains in [`linguistic_variables.py`](../src/greenrisk/linguistic_variables.py) as a documented, reproducible counterfactual, outside the scoring path. It is the overlay in the paper's membership-function figure (`artifacts/figures/mf_specificity_trap.png`). @@ -274,9 +277,11 @@ through low specificity. Distinct from the commitment tail, also upstream. **Date:** 2026-06-16 · **Annotated tag:** `rulebase-locked-v1` · **Commit:** `a40288a` -The rules and the membership functions are frozen together, once, before any -contact with the held-out contrast set. Anything changed after this point is a -separate, logged instrument version — never a silent edit. +The rules and membership functions are frozen together. Git history establishes +that the contrast scores were produced after this freeze and that no later +instrument edit fitted the rules to those results. Claims about when humans +first inspected a locally available case file are process statements rather +than independently verifiable facts. **Locked state** @@ -288,7 +293,7 @@ separate, logged instrument version — never a silent edit. `sentiment_asymmetry = P('opportunity')`, `netzero = P('net-zero')`; climate gate = detector `P('yes') ≥ 0.5`. - **Models:** five pinned ClimateBERT revisions, recorded in - [`models.py`](../models.py) and re-recorded in every run manifest and PROV-O + [`metadata.py`](../src/greenrisk/metadata.py) and re-recorded in every run manifest and PROV-O graph. **Calibration anchor, re-verified on the locked instrument** @@ -383,17 +388,18 @@ bingler_stats.json}`, `artifacts/figures/bingler_{convergence,discriminant}.png` ## DL-005 — held-out face validity and the symmetric specificity limitation -**Date:** 2026-06-17 · **Trigger:** the one-shot held-out test against the -contrast set — the payoff of the lock discipline. Harnesses: +**Date:** 2026-06-17 · **Trigger:** the post-lock contrast evaluation. Harnesses: `scripts/run_contrast_set.py`, `scripts/evaluate_face_validity.py`, `scripts/provenance_contrast_run.py`. Instrument unchanged — analysis only. -### Adequacy gate, performed blind +### Adequacy gate (historical process account) -The contrast set (15 paragraph-level cases: 9 greenwashing from DWS, +The original project record states that the contrast set (15 paragraph-level cases: 9 greenwashing from DWS, Volkswagen and HSBC, regulator-adjudicated; 6 rigorous from Microsoft and Ørsted, CDP A-list and SBTi-validated) was inspected **read-only, with no model -run**, before scoring. Three findings shaped the test: +run**, before scoring. Git history can verify the instrument freeze and absence +of post-lock tuning, but not that sequence of local inspection. Three findings +were recorded: 1. Granularity is already paragraph-level — score directly, no aggregation rule needed. @@ -405,8 +411,8 @@ run**, before scoring. Three findings shaped the test: That third finding is why the set was **stratified before scoring**. Lumping all 9 greenwashing cases against all 6 reference cases would conflate two constructs and misread predictable, in-principle misses as failures of the -primary claim. The full pre-registered stratification is reproduced in -[Appendix A](#appendix-a--phase-6-pre-registration-ratified-blind). +primary claim. The recorded stratification is reproduced in +[Appendix A](#appendix-a--recorded-phase-6-expectations). ### Results, one-shot, against the frozen instrument @@ -417,8 +423,8 @@ primary claim. The full pre-registered stratification is reproduced in regulator-grounded sourcing, not on the p-value. - **Boundary** — the 4 greenwashing cases held outside the primary comparison (3 out-of-scope specificity-based, 1 boundary promotional) all scored - **below the flag threshold of 50**, as predicted in advance. The blind spot is - bounded and predictable. + **below the flag threshold of 50**, matching the recorded expectations. The + failure mode is bounded within this diagnostic set. - At that threshold, sensitivity within the in-scope group is 3/5 (0.60) and specificity within the reference group is 5/6 (0.83). @@ -436,7 +442,7 @@ rather than hypothesised: at ≈ 10 via S9: selective or corrupted numbers route to the low-risk cells because the paragraph *reads* concrete. -Two further misses were pre-registered as upstream-signal caveats, and are what +Two further misses were recorded as upstream-signal caveats, and are what sensitivity 3/5 is made of: **`GW-007`**, a two-sentence advertisement well below the models' typical training length, where the specificity model read the "$1 trillion" figure as substance (10.88, S9); and **`GW-002`**, generic ESG @@ -455,19 +461,19 @@ scores were seen. **Artifacts:** `artifacts/contrast_run/{contrast_scored.csv, run_manifest.json, face_validity_stats.json}`, `artifacts/figures/face_validity.png`, `artifacts/provenance/contrast_run.{ttl,json}` (which records -`prereg_ratified`). +the protocol status and expectations date). --- -## Appendix A — Phase-6 pre-registration, ratified blind +## Appendix A — recorded Phase-6 expectations -Ratified **2026-06-17**, from the curator notes and the locked design -philosophy, with **no model run on the contrast set** and the instrument already -frozen. The date is recorded independently in -`artifacts/contrast_run/run_manifest.json` (`prereg_ratified`) and in the -contrast-run PROV-O graph. +Recorded **2026-06-17** from curator notes and the locked design philosophy. +The instrument was already frozen and was not changed after the results. The +expectations and first results appear in the same public commit, so the public +repository does not supply an independent timestamp for their ordering. The +manifest and PROV-O graph preserve this exact protocol status. -| Case | Company | Mechanism | Stratum | Pre-registered expectation | +| Case | Company | Mechanism | Stratum | Recorded expectation | | --- | --- | --- | --- | --- | | GW-001, GW-002, GW-003 | DWS | vague aspirational | **in_scope** | elevated | | GW-005 | Volkswagen | process-talk, no commitment | **in_scope** | moderate–elevated | @@ -483,6 +489,36 @@ vs. reference separation, with the boundary probe reported separately; disagreements inside a stratum are logged as limitations, never as instrument edits; and the stratification is not revised after the scores are seen. -Both pre-registered caveats — GW-007's length and RD-006's process framing — -turned out to be exactly where the instrument failed. They are recorded here as -predictions made before the fact, not as explanations found afterwards. +Both recorded caveats — GW-007's length and RD-006's process framing — align +with observed failures. Because the public timestamps are simultaneous, this is +reported as protocol documentation rather than independent prospective proof. + +--- + +## DL-006 — repository hardening and claim boundaries + +**Date:** 2026-09-04 · **Trigger:** end-to-end repository, packaging, +provenance, and evidence-claim audit. Instrument unchanged — software and +documentation only. + +The audit found four infrastructure gaps: the project was not buildable as a +package, normal dependency installation pulled the full research/GPU stack, +there was no automated CI, and provenance hashes reflected a CRLF working tree +rather than Git-normalized artifact bytes. It also found two over-broad claims: +the learned classifier stages were grouped under “nothing is a black box,” and +the contrast protocol was described as independently blind even though its +expectations and first results share one public commit. + +**Decision.** Move code to an installable `src/greenrisk` package; split base, +model, research, and development dependencies; add validated CLI contracts, +pytest, lint/build CI, manual model smoke tests, pinned dataset coordinates, +complete future-run manifests, and hash/PROV-O integrity checks. Normalize text +artifacts to LF. Describe the fuzzy layer as transparent while explicitly +retaining learned-model uncertainty. Describe Phase 6 as a post-lock contrast +evaluation whose expectations were not used for instrument tuning, without +claiming independently timestamped preregistration. + +Historical manifests were expanded only from immutable Git and Hugging Face +records. Unknown runtime details are marked unavailable. The numerical content +of `MODEL_REGISTRY`, `SIGNAL_MAP`, the membership functions, and `RULES` remains +the `rulebase-locked-v1` instrument; no ablation was performed. diff --git a/docs/responsible-use.md b/docs/responsible-use.md new file mode 100644 index 0000000..4f482e4 --- /dev/null +++ b/docs/responsible-use.md @@ -0,0 +1,53 @@ +# Responsible use + +GreenRisk is a research screening instrument for paragraph-level corporate +climate disclosures. It can help researchers and reviewers prioritize passages +for closer inspection and study how a fixed set of linguistic constructs behave. + +## Appropriate uses + +- exploratory research and method comparison; +- triage that leads to human review of the full disclosure and supporting data; +- reproducibility studies using the pinned models and instrument; +- sensitivity, fairness, robustness, and ablation analysis reported with scope. + +## Inappropriate uses + +- asserting that an organization has legally or factually committed + greenwashing based on a score; +- automated enforcement, investment, credit, employment, or other high-impact + decisions without independent evidence and accountable human review; +- ranking organizations from isolated paragraphs as if the scores measured + organization-wide conduct; +- removing the climate gate, overriding it, or changing thresholds without + recording the change; +- presenting the learned ClimateBERT classifiers as fully interpretable. + +## Known limitations + +GreenRisk operationalizes risk mainly through specificity, commitment, +opportunity framing, and net-zero language. A false but detailed claim may score +low, while a cautious but short legitimate statement may score higher. The +instrument does not verify facts, detect material omissions, assess supplied +evidence, or model the broader reporting context. All upstream classifier +errors flow into the fuzzy layer. Performance can vary by language, sector, +jurisdiction, document type, and time; only English climate-disclosure text is +represented in the committed evaluation. + +The reported contrast evaluation is small. Its expectations were not used to +tune the post-lock instrument, but they were published in the same commit as +the first results, so it is not an externally timestamped blind +preregistration. The corpus comparison also shares classifier-derived +constructs with its baseline and is not independent criterion validation. + +## Review checklist + +Before relying on a score, retain the source paragraph and document context, +inspect the climate-gate decision, review all fired rules rather than only the +top rule, check model/instrument revisions, corroborate claims with external +evidence, and document who made the final judgment. When `--force` is used, +record why the climate gate was overridden. + +Security or harmful-use concerns can be reported through GitHub's private +vulnerability-reporting channel if enabled; scientific limitations belong in a +public issue with a minimal reproducible example and no sensitive data. diff --git a/docs/validation.md b/docs/validation.md index f1f6416..658259e 100644 --- a/docs/validation.md +++ b/docs/validation.md @@ -1,136 +1,100 @@ -# Validation - -This document lays out the evidence for GreenRisk's validity, in two -independent layers: a large-scale statistical comparison against a validated -baseline, and a held-out, case-level face-validity test against real, -regulator-adjudicated examples. It also states the instrument's scope limit -plainly, with the specific cases that expose it. - -Both layers were run against the same **frozen instrument** -(`rulebase-locked-v1`, see [Architecture §7](architecture.md#7-the-locked-instrument); -the decisions that produced it are recorded in [`decisions.md`](decisions.md)). -The held-out case set (Layer 2) was not inspected or scored until after the -instrument was frozen, and its evaluation criteria were fixed in advance of -scoring — the point of freezing the instrument first is that neither layer of -evidence could have been produced by tuning the rules to fit the answer. - -## Layer 1 — large-scale comparison against a validated baseline - -**Setup.** 1,300 paragraphs from the `climatebert/tcfd_recommendations` corpus -were passed through the climate-relevance gate; 1,009 passed and were scored -by the full pipeline. Each paragraph's score was compared against an -established external baseline: the "cheap talk" measure from Bingler et al. -(`1 - P('spec')`, i.e. one minus the specificity model's own output), the -metric introduced and validated in the paper the specificity classifier itself -comes from (see [Acknowledgements](acknowledgements.md)). - -**Convergent validity.** GreenRisk's score correlates strongly with cheap -talk: Spearman ρ = 0.60 (p ≈ 1.4×10⁻¹⁰⁰, n = 1,009). This establishes that -GreenRisk is tracking the same underlying signal as an already-validated -method, not measuring noise. - -**Discriminant validity.** Restricting to the most vague quartile of text by -the cheap-talk measure (n = 253, cheap-talk mean 0.96, standard deviation -0.01 — a group the baseline treats as essentially uniform), GreenRisk's score -still spans 35–90 (mean 38.6, sd 13.1), correlated with the commitment signal -at Spearman ρ = 0.43. Where the baseline sees a single undifferentiated mass -of vague text, GreenRisk separates "vague and quiet" (a weak disclosure) from -"vague and loud" (a confident, unsubstantiated pledge). This is the concrete, -measured value the four-signal design adds over the single-signal baseline. - -**The convergence curve is not monotone, and that is by design.** Mean risk -climbs with cheap talk through the middle deciles but bends down in the most -vague decile (cheap-talk ≈ 0.94–0.97 → mean risk drops to the high 30s/low -40s, versus roughly 50 at cheap-talk ≈ 0.82–0.90). This is exactly the -Tier-1 spine's S1 rule surfacing at scale: maximally vague text that makes no -claim is deliberately scored lower than moderately vague text that does. The -divergence from a naively monotone baseline is a designed property of the -rule base, not an artifact. - -**Descriptive cut by disclosure category.** Scored by TCFD category, metrics -sections are concrete (mean cheap-talk 0.20, mean risk 21.0) while strategy, -risk, and governance sections are markedly more vague (cheap-talk 0.65–0.87, -risk 37–44) — a pattern consistent with prior "cherry-picking" findings in the -climate-disclosure literature, reproduced here on GreenRisk's own score. - -## Layer 2 — held-out face validity against real, adjudicated cases - -**The case set.** `data/contrast_set.csv` contains 15 paragraph-level cases -built entirely from public sources, each with its own citation: - -- 9 greenwashing cases, drawn from enforcement actions and rulings against - **DWS Group** (SEC settlement; German Frankfurt Public Prosecutor's Office - fine), **Volkswagen AG** (U.S. EPA/DOJ "Dieselgate" action), and **HSBC UK - Bank plc** (UK Advertising Standards Authority ruling); -- 6 reference cases of rigorous disclosure, from **Microsoft** (CDP A-list, - SBTi-validated) and **Ørsted A/S** (CDP A-list, SBTi-validated). - -**Two mechanisms, one in scope.** Inspecting the case set before scoring -surfaced an important structural fact: the greenwashing cases split into two -distinct mechanisms. **Vagueness-based greenwashing** ("cheap talk" — -aspirational language with no specifics) is exactly what the four-signal -design targets. **Specificity-based greenwashing** — concrete, detailed -figures that are nonetheless selective, corrupted, or misleading in what they -omit — works through a mechanism the instrument's signals cannot see, since -`specificity` reads a paragraph's surface form, not the truthfulness of its -numbers. The case set was stratified into **in-scope** (vagueness-based), -**boundary** (a promotional/awards case, probing the opportunity-framing -tier), and **out-of-scope** (specificity-based) groups before any scoring took -place, so that out-of-scope misses are read as a documented scope limit rather -than as a failure of the primary claim. The full stratification, with the -per-case expectation registered for each of the 15 cases, is published in -[Appendix A of the decision record](decisions.md#appendix-a--phase-6-pre-registration-ratified-blind); -its ratification date is independently recorded in -`artifacts/contrast_run/run_manifest.json` and in the contrast-run PROV-O graph. - -**Primary result.** In-scope greenwashing separates cleanly from the rigorous -reference group: AUC = 0.87 (n = 5 vs. 6; Mann-Whitney U = 26, one-sided -p = 0.026). The sample is small by design — held-out, hand-curated, -regulator-grounded case sets do not scale the way a corpus run does — so this -is reported as a demonstration with a large effect size, not as a -statistically definitive result on its own; it is the second, independent -layer of evidence alongside Layer 1's corpus-scale statistics, not a -replacement for it. At the flag threshold of 50, sensitivity within the -in-scope group is 3/5 (0.60) and specificity within the reference group is -5/6 (0.83); the individual misses are itemized in the scope-limitation -section below. - -**Boundary result.** All 4 greenwashing cases held outside the primary -comparison scored below the flag threshold of 50, exactly as predicted in -advance: the 3 out-of-scope, specificity-based cases (`GW-004`, `GW-008`, -`GW-009`) and the 1 boundary promotional case (`GW-006`). This confirms that -the instrument's blind spot is precise and predictable rather than arbitrary. - -## Scope limitation: specificity is a proxy, and proxies can be fooled - -`specificity` measures whether a paragraph *reads* as concrete — numbers, -dates, named mechanisms — not whether those numbers are honest or complete. -That proxy can fail in both directions, and both failure modes are visible in -the held-out cases rather than hypothetical: - -- **Low specificity does not always mean low substance.** Case `RD-006` - (Ørsted, describing its preparation for EU CSRD/ESRS reporting) scored 72.7 - — Elevated — because it uses confident commitment language while describing - a *process* rather than citing figures. It is a false positive against a - rigorous discloser, produced by the same signature (vague-but-committed) - that correctly flags genuine greenwashing elsewhere. -- **High specificity does not always mean good faith.** Cases `GW-004` and - `GW-008` — Volkswagen's tailpipe-emissions figures published during the - period its defeat-device software was active, and HSBC's tree-planting - commitment set against financed fossil-fuel emissions roughly fifty times - larger — both scored Low. The paragraphs are specific; what they omit is - not visible to a paragraph-level text classifier. - -Two further upstream-signal cases are documented for completeness: `GW-007` -(a short advertising sentence, below the typical training-length paragraph, -where the specificity model misread a dollar figure) and `GW-002` (generic -ESG prose where the commitment classifier read near-zero commitment despite -the surrounding case being part of a documented misrepresentation). - -**The resulting, precisely bounded claim:** GreenRisk detects -vagueness-based ("cheap talk") greenwashing, with measured effect sizes at -both corpus scale and case-study scale. It is not designed to detect, and -does not reliably detect, omission- or fraud-based greenwashing where the -disclosed numbers themselves are the problem. Catching that second mechanism -would require verifying claims against external data, not just reading the -paragraph — a different problem from the one this instrument solves. +# Validation and evidence + +GreenRisk has two exploratory evidence layers, both produced with the numerical +instrument frozen at `rulebase-locked-v1`. They characterize whether the +instrument behaves consistently with its intended construct and expose its +failure modes. Neither layer establishes organization-level, causal, legal, or +population-wide validity. + +## Evidence hierarchy + +| Layer | Data | What it supports | What it does not support | +| --- | --- | --- | --- | +| Corpus characterization | 1,009 climate-gated TCFD paragraphs | construct alignment and incremental differentiation | independent criterion validity or firm-level accuracy | +| Post-lock contrast evaluation | 15 sourced paragraph cases | face validity and concrete boundary failures | a general benchmark or externally preregistered estimate | +| Property tests | 4,158 fuzzy-grid evaluations plus trace checks | implementation consistency of locked rule behavior | empirical truth of the construct | + +## Layer 1 — corpus characterization + +The full train split of pinned dataset `climatebert/tcfd_recommendations` +(revision `aee5c98f0bf7835bfb08308ffc7c17216657976b`) contains 1,300 +paragraphs. The climate gate retained 1,009 at `P(yes) ≥ 0.5`. + +GreenRisk was compared with the Bingler et al. cheap-talk measure, +`1 - P(spec)`. Spearman correlation was ρ = 0.602 (p ≈ 1.4×10⁻¹⁰⁰, +n = 1,009). Within the baseline's vaguest quartile (n = 253; cheap-talk mean +0.961, sd 0.010), GreenRisk ranged from 35 to 90 and correlated with commitment +at ρ = 0.433. This is consistent with the intended distinction between quiet +vagueness and confident, unsupported pledges. + +This comparison is not an independent validation target: the baseline uses the +same specificity classifier that supplies one GreenRisk input. The findings +are best interpreted as convergent construct alignment plus evidence that the +additional commitment/rule structure differentiates passages the baseline +treats similarly. TCFD category cuts are descriptive only because the dataset +has no firm or document identifiers and no greenwashing labels. + +The non-monotone bend in the highest cheap-talk deciles is expected. Rule S1 +assigns vague text with no claim to Moderate risk; louder vague claims reach +higher terms. This is a frozen design property, not an empirical correction. + +## Layer 2 — post-lock contrast evaluation + +`data/contrast_set.csv` contains 15 sourced excerpts: nine passages associated +with public greenwashing enforcement/rulings involving DWS, Volkswagen, and +HSBC, and six reference passages from Microsoft and Ørsted disclosures. It is +a hand-curated diagnostic set, not a representative sample. + +The numerical instrument had been frozen before this evaluation and the case +expectations were not used to tune it. However, the expectations and first +results were introduced together in public Git commit `8b2f461`. The repository +therefore cannot independently demonstrate an externally timestamped blind +preregistration. The table in +[`decisions.md`](decisions.md#appendix-a--recorded-phase-6-expectations) is +retained as the recorded protocol, with this chronology limitation explicit. + +For the primary in-scope comparison—five vagueness-based cases versus six +reference passages—the observed AUC was 0.867 (Mann–Whitney U = 26, one-sided +p = 0.026). At threshold 50, sensitivity was 3/5 and specificity 5/6. Because +the sample is tiny, selected, and paragraph-level, these values are descriptive +effect estimates rather than general performance guarantees. + +All four cases outside the primary scope scored below 50: three detailed but +misleading/omissive passages and one promotional boundary case. This is useful +negative evidence. It shows that linguistic specificity can route a passage to +low risk even when external facts make it misleading. + +## Documented failure modes + +- `RD-006` scored 72.7: process-oriented reporting language had strong + commitment but few paragraph-level specifics, producing a false positive. +- `GW-004` and `GW-008` scored near 10: concrete numbers appeared specific even + though the broader adjudicated context involved corrupted measurement or + omission. +- `GW-007` scored 10.88: a short advertisement with a dollar figure was read as + specific. +- `GW-002` scored 35.0: the upstream commitment classifier returned 0.006 on + generic ESG prose. + +The defensible scope is therefore narrow: GreenRisk screens for patterns of +vagueness, claim strength, opportunity framing, and net-zero language. It does +not establish truth, detect material omission, or verify whether numbers and +actions match. See [`responsible-use.md`](responsible-use.md). + +## Reproducibility + +Committed outputs are bound by SHA-256 in versioned manifests and PROV-O: + +- `artifacts/corpus_run/` and `artifacts/provenance/corpus_run.*` +- `artifacts/contrast_run/` and `artifacts/provenance/contrast_run.*` + +Run `uv run python scripts/verify_artifact_integrity.py` to verify these links. +The historical manifests identify exact pipeline/instrument commits, model and +dataset revisions, lockfile hash, and result hashes. Runtime details not +captured by the original scripts are explicitly marked unavailable. New runs +record Python, PyTorch, Transformers, device, lockfile, input, and output +coordinates automatically. + +The locked fuzzy behavior is asserted by pytest: rule integrity, rule-term +ordering, bounded centroid artifacts, bounded amplifier exceptions, and +agreement between independently re-derived and scikit-fuzzy firing strengths. diff --git a/main.py b/main.py deleted file mode 100644 index f78c6a9..0000000 --- a/main.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Command-line scorer for already-computed GreenRisk signals.""" - -from __future__ import annotations - -import argparse - -from rule_base import ANTS, format_trace, score_paragraph - - -def probability(value: str) -> float: - parsed = float(value) - if not 0.0 <= parsed <= 1.0: - raise argparse.ArgumentTypeError("expected a probability in [0, 1]") - return parsed - - -def build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser( - description="Score one paragraph from precomputed GreenRisk signal probabilities." - ) - parser.add_argument("--specificity", required=True, type=probability) - parser.add_argument("--commitment", required=True, type=probability) - parser.add_argument( - "--sentiment-asymmetry", - dest="sentiment_asymmetry", - required=True, - type=probability, - ) - parser.add_argument("--netzero", required=True, type=probability) - return parser - - -def main() -> None: - args = build_parser().parse_args() - signals = {name: getattr(args, name) for name in ANTS} - risk, trace = score_paragraph(signals) - print(format_trace(signals, risk, trace)) - - -if __name__ == "__main__": - main() diff --git a/pyproject.toml b/pyproject.toml index d035c7b..993fbf9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [project] name = "greenrisk" -version = "0.1.0" -description = "Fuzzy multi-dimensional greenwashing risk scoring pipeline" +version = "0.2.0" +description = "Explainable-by-construction greenwashing-risk scoring for climate disclosures" authors = [ { name = "Isaac D'Césares", email = "isaac.dcesares@ufrj.br" } ] @@ -9,33 +9,68 @@ readme = "README.md" license = "Apache-2.0" requires-python = ">=3.12" dependencies = [ + "networkx>=3.6.1", + "numpy>=2.3.0", + "prov>=2.1.1", + "rdflib>=7.6.0", + "scikit-fuzzy>=0.5.0", + "scipy>=1.17.1", +] + +[project.optional-dependencies] +models = [ + "huggingface-hub>=1.13.0", + "torch>=2.11.0", + "transformers>=5.7.0", +] +research = [ "datasets>=4.8.5", "graphviz>=0.21", - "huggingface-hub>=1.13.0", "jupyterlab>=4.5.7", "lxml>=5.0.0", "matplotlib>=3.11.0", "networkx>=3.6.1", + "pandas>=3.0.0", "prov>=2.1.1", "pydot>=4.0.1", - "python-dotenv>=1.2.2", - "rdflib>=7.6.0", "scikit-fuzzy>=0.5.0", "scipy>=1.17.1", "torch>=2.11.0", "transformers>=5.7.0", ] +[project.scripts] +greenrisk = "greenrisk.cli:main" + [project.urls] Homepage = "https://github.com/idcesares/GreenRisk" Repository = "https://github.com/idcesares/GreenRisk" +Documentation = "https://github.com/idcesares/GreenRisk/tree/master/docs" -[[tool.uv.index]] -name = "pytorch-cu128" -url = "https://download.pytorch.org/whl/cu128" -explicit = true +[build-system] +requires = ["setuptools>=80"] +build-backend = "setuptools.build_meta" -[tool.uv.sources] -torch = [ - { index = "pytorch-cu128", marker = "sys_platform == 'win32' or sys_platform == 'linux'" }, +[dependency-groups] +dev = [ + "build>=1.3.0", + "pytest>=9.0.0", + "ruff>=0.13.0", ] + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = ["*_test_*.py", "test_*.py"] +markers = [ + "model: downloads and executes pinned ClimateBERT models", +] + +[tool.ruff] +line-length = 100 +target-version = "py312" + +[tool.ruff.lint] +select = ["E4", "E7", "E9", "F", "I", "B"] diff --git a/scripts/bingler_baseline.py b/scripts/bingler_baseline.py index d323cea..7110a29 100644 --- a/scripts/bingler_baseline.py +++ b/scripts/bingler_baseline.py @@ -24,10 +24,9 @@ sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) -import numpy as np -import pandas as pd import matplotlib.pyplot as plt -from scipy.stats import spearmanr, pearsonr +import pandas as pd +from scipy.stats import pearsonr, spearmanr ROOT = pathlib.Path(__file__).resolve().parents[1] CSV = ROOT / "artifacts" / "corpus_run" / "tcfd_scored.csv" diff --git a/scripts/evaluate_face_validity.py b/scripts/evaluate_face_validity.py index 4eabcd6..de3b39c 100644 --- a/scripts/evaluate_face_validity.py +++ b/scripts/evaluate_face_validity.py @@ -1,11 +1,11 @@ """Formal face-validity evaluation of the contrast-set scores. -Reads the one-shot scores (no model re-run) and evaluates per the FROZEN -pre-registration (see docs/validation.md for the stratification rationale): +Reads the historical scores (no model re-run) and evaluates per the recorded +expectations (see docs/validation.md for the protocol and chronology limit): PRIMARY - separation of in-scope greenwashing vs rigorous (AUC + Mann-Whitney) BOUNDARY - out-of-scope greenwashing not flagged (all risk < elevated threshold) Small n: a demonstration, not a benchmark - reported with that caveat. Errors vs. -the pre-registration are logged findings (limitations), never instrument fixes. +the expectations are logged findings (limitations), never instrument fixes. Inputs: artifacts/contrast_run/contrast_scored.csv Outputs: artifacts/figures/face_validity.png @@ -19,9 +19,9 @@ sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) +import matplotlib.pyplot as plt import numpy as np import pandas as pd -import matplotlib.pyplot as plt from matplotlib.lines import Line2D from scipy.stats import mannwhitneyu @@ -32,9 +32,12 @@ def band(r): - if r < 22.5: return "Low" - if r < 50.0: return "Moderate" - if r < 77.5: return "Elevated" + if r < 22.5: + return "Low" + if r < 50.0: + return "Moderate" + if r < 77.5: + return "Elevated" return "High" @@ -91,7 +94,10 @@ def main(): ax.set_xticklabels(order) ax.set_ylabel("GreenRisk risk") ax.set_ylim(0, 100) - ax.set_title(f"Held-out face validity (primary AUC={auc:.2f}; boundary {not_flagged}/{len(oos)})") + ax.set_title( + f"Post-lock contrast evaluation " + f"(primary AUC={auc:.2f}; boundary {not_flagged}/{len(oos)})" + ) ax.legend(handles=[ Line2D([0], [0], marker='o', color='w', markerfacecolor=colors['greenwashing'], markeredgecolor='k', label='greenwashing', markersize=9), @@ -121,4 +127,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/first_provenance.py b/scripts/first_provenance.py index 7f4c384..e44bb1b 100644 --- a/scripts/first_provenance.py +++ b/scripts/first_provenance.py @@ -1,14 +1,18 @@ """Emit the foundation PROV-O graph for one detector inference example.""" +from __future__ import annotations + import hashlib import os import shutil -from datetime import datetime, timezone +from datetime import UTC, datetime from pathlib import Path +from typing import TYPE_CHECKING -from prov.dot import prov_to_dot -from prov.model import Namespace, ProvDocument +from greenrisk import __version__ +if TYPE_CHECKING: + from prov.model import ProvDocument TEST_PARAGRAPH = ( "We are committed to achieving net-zero greenhouse gas emissions across " @@ -54,14 +58,16 @@ def build_first_provenance( ) -> ProvDocument: """Build a minimal PROV-O graph for one paragraph-level model inference.""" + from prov.model import Namespace, ProvDocument + doc = ProvDocument() - gr = Namespace("gr", "https://greenrisk.ppgi.ufrj.br/prov/") + gr = Namespace("gr", "https://github.com/idcesares/GreenRisk/prov/") hf = Namespace("hf", "https://huggingface.co/") - doc.set_default_namespace("https://greenrisk.ppgi.ufrj.br/prov/") + doc.set_default_namespace("https://github.com/idcesares/GreenRisk/prov/") doc.add_namespace(gr) doc.add_namespace(hf) - para_hash = hashlib.sha256(paragraph_text.encode()).hexdigest()[:12] + para_hash = hashlib.sha256(paragraph_text.encode()).hexdigest() model_slug = model_name.split("/")[-1] paragraph = doc.entity( @@ -91,7 +97,7 @@ def build_first_provenance( }, ) - timestamp = datetime.now(timezone.utc) + timestamp = datetime.now(UTC) inference = doc.activity( f"gr:inference_{para_hash}_{model_slug}", timestamp, @@ -105,10 +111,10 @@ def build_first_provenance( }, ) pipeline = doc.agent( - "gr:GreenRiskPipeline_v0.1", + f"gr:GreenRiskPipeline_v{__version__}", { "prov:type": "prov:SoftwareAgent", - "gr:version": "0.1.0", + "gr:version": __version__, }, ) @@ -122,6 +128,8 @@ def build_first_provenance( def write_outputs(doc: ProvDocument, output_dir: Path) -> None: + from prov.dot import prov_to_dot + output_dir.mkdir(parents=True, exist_ok=True) ttl_path = output_dir / "first_provenance.ttl" diff --git a/scripts/pin_models.py b/scripts/pin_models.py deleted file mode 100644 index 43d0d80..0000000 --- a/scripts/pin_models.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Look up current commit hashes for all five ClimateBERT models we depend on. -Run this once at project setup. Pin the resulting hashes in models.py.""" - -from huggingface_hub import HfApi - -MODELS = [ - "climatebert/distilroberta-base-climate-detector", - "climatebert/distilroberta-base-climate-specificity", - "climatebert/distilroberta-base-climate-commitment", - "climatebert/distilroberta-base-climate-sentiment", - "climatebert/netzero-reduction", -] - -api = HfApi() -print("Current commit hashes (copy these into models.py):\n") -for repo_id in MODELS: - info = api.model_info(repo_id) - print(f' "{repo_id}": "{info.sha}",') \ No newline at end of file diff --git a/scripts/plot_linguistic_variables.py b/scripts/plot_linguistic_variables.py index cc7e6c0..5c0cd61 100644 --- a/scripts/plot_linguistic_variables.py +++ b/scripts/plot_linguistic_variables.py @@ -5,23 +5,25 @@ """ import pathlib -import sys - -# Repo root on the path so core modules import when run from anywhere. -ROOT = pathlib.Path(__file__).resolve().parents[1] -sys.path.insert(0, str(ROOT)) -FIG_DIR = ROOT / "artifacts" / "figures" -FIG_DIR.mkdir(parents=True, exist_ok=True) import matplotlib + matplotlib.use("Agg") # render to file; no GUI windows, no blocking import matplotlib.pyplot as plt -from linguistic_variables import ( - specificity, commitment, sentiment_asymmetry, netzero, risk, +from greenrisk.linguistic_variables import ( + commitment, + netzero, + risk, + sentiment_asymmetry, + specificity, specificity_trap, ) +ROOT = pathlib.Path(__file__).resolve().parents[1] +FIG_DIR = ROOT / "artifacts" / "figures" +FIG_DIR.mkdir(parents=True, exist_ok=True) + # filename -> the variable to plot FIGURES = { "mf_specificity.png": specificity, @@ -37,4 +39,4 @@ fig = plt.gcf() # grab the figure view() just created fig.savefig(FIG_DIR / filename, dpi=150, bbox_inches="tight") plt.close(fig) # free it; don't leak figures in the loop - print(f"saved {FIG_DIR / filename}") \ No newline at end of file + print(f"saved {FIG_DIR / filename}") diff --git a/scripts/provenance_contrast_run.py b/scripts/provenance_contrast_run.py index 323cdf9..410e0ed 100644 --- a/scripts/provenance_contrast_run.py +++ b/scripts/provenance_contrast_run.py @@ -9,15 +9,11 @@ import hashlib import json import pathlib -import sys from datetime import datetime -sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) +from prov.model import Namespace, ProvDocument -from prov.model import ProvDocument, Namespace - -from models import MODEL_REGISTRY, SIGNAL_MAP -from rule_base import RULES +from greenrisk.metadata import INSTRUMENT_METHOD, INSTRUMENT_RULE_COUNT ROOT = pathlib.Path(__file__).resolve().parents[1] RUN_DIR = ROOT / "artifacts" / "contrast_run" @@ -34,28 +30,34 @@ def sha256_file(path): def main(): - manifest = json.loads(MANIFEST.read_text()) + manifest = json.loads(MANIFEST.read_text(encoding="utf-8")) when = datetime.fromisoformat(manifest["timestamp_utc"]) doc = ProvDocument() - gr = Namespace("gr", "https://greenrisk.ppgi.ufrj.br/prov/") + gr = Namespace("gr", "https://github.com/idcesares/GreenRisk/prov/") hf = Namespace("hf", "https://huggingface.co/") - doc.set_default_namespace("https://greenrisk.ppgi.ufrj.br/prov/") + doc.set_default_namespace("https://github.com/idcesares/GreenRisk/prov/") doc.add_namespace(gr) doc.add_namespace(hf) - agent = doc.agent("gr:GreenRiskPipeline_v0.2", - {"prov:type": "prov:SoftwareAgent", "gr:version": "0.2.0"}) + version = manifest["software_version"] + agent = doc.agent( + f"gr:GreenRiskPipeline_v{version}", + {"prov:type": "prov:SoftwareAgent", "gr:version": version}, + ) instrument = doc.entity("gr:instrument_" + manifest["instrument_tag"], - {"prov:type": "gr:FuzzyInstrument", + {"prov:type": "gr:FuzzyInstrument", "gr:tag": manifest["instrument_tag"], - "gr:n_rules": str(len(RULES)), - "gr:defuzz": "Mamdani/centroid"}) + "gr:commit": manifest["instrument_commit"], + "gr:n_rules": str(INSTRUMENT_RULE_COUNT), + "gr:defuzz": INSTRUMENT_METHOD}) dataset = doc.entity("gr:contrast_set", - {"prov:type": "gr:HeldOutContrastSet", - "gr:name": manifest["dataset"], + {"prov:type": "gr:HeldOutContrastSet", + "gr:name": manifest["dataset"], + "gr:sha256": manifest["dataset_sha256"], "gr:n": str(manifest["n"]), - "gr:prereg_ratified": manifest["prereg_ratified"]}) + "gr:protocol_status": manifest["protocol_status"], + "gr:expectations_recorded": manifest["expectations_recorded"]}) run = doc.activity("gr:contrast_run", when, when, {"prov:type": "gr:HeldOutEvaluation", "gr:gate": str(manifest["gate"]), @@ -65,12 +67,12 @@ def main(): doc.used(run, dataset) doc.used(run, instrument) - for short in ["detector"] + [SIGNAL_MAP[v][0] for v in SIGNAL_MAP]: - repo = MODEL_REGISTRY[short]["repo"] + for short, commit in manifest["model_revisions"].items(): + repo = manifest["model_repositories"][short] e_model = doc.entity("hf:" + repo, {"prov:type": "gr:PretrainedModel", "gr:short_name": short, - "gr:commit_hash": MODEL_REGISTRY[short]["revision"]}) + "gr:commit_hash": commit}) doc.used(run, e_model) for fname, ptype in [("contrast_scored.csv", "gr:ScoredContrastSet"), @@ -78,9 +80,16 @@ def main(): fpath = RUN_DIR / fname if not fpath.exists(): continue + expected_hash = manifest["outputs"][fname]["sha256"] + actual_hash = sha256_file(fpath) + if actual_hash != expected_hash: + raise ValueError( + f"artifact integrity failure for {fpath}: " + f"expected {expected_hash}, got {actual_hash}" + ) e = doc.entity("gr:" + fname.replace(".", "_"), {"prov:type": ptype, "gr:filename": fname, - "gr:sha256": sha256_file(fpath)}) + "gr:sha256": expected_hash}) doc.wasGeneratedBy(e, run) doc.wasDerivedFrom(e, dataset) doc.wasDerivedFrom(e, instrument) @@ -93,4 +102,4 @@ def main(): if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/scripts/provenance_corpus_run.py b/scripts/provenance_corpus_run.py index b987e92..10e02a2 100644 --- a/scripts/provenance_corpus_run.py +++ b/scripts/provenance_corpus_run.py @@ -18,15 +18,11 @@ import hashlib import json import pathlib -import sys from datetime import datetime -sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) +from prov.model import Namespace, ProvDocument -from prov.model import ProvDocument, Namespace - -from models import MODEL_REGISTRY, SIGNAL_MAP -from rule_base import RULES +from greenrisk.metadata import INSTRUMENT_METHOD, INSTRUMENT_RULE_COUNT ROOT = pathlib.Path(__file__).resolve().parents[1] RUN_DIR = ROOT / "artifacts" / "corpus_run" @@ -44,31 +40,42 @@ def sha256_file(path): def main(): - manifest = json.loads(MANIFEST.read_text()) + manifest = json.loads(MANIFEST.read_text(encoding="utf-8")) csv_hash = sha256_file(CSV) + expected_hash = manifest["outputs"][CSV.name]["sha256"] + if csv_hash != expected_hash: + raise ValueError( + f"artifact integrity failure for {CSV}: expected {expected_hash}, got {csv_hash}" + ) when = datetime.fromisoformat(manifest["timestamp_utc"]) doc = ProvDocument() - gr = Namespace("gr", "https://greenrisk.ppgi.ufrj.br/prov/") + gr = Namespace("gr", "https://github.com/idcesares/GreenRisk/prov/") hf = Namespace("hf", "https://huggingface.co/") - doc.set_default_namespace("https://greenrisk.ppgi.ufrj.br/prov/") + doc.set_default_namespace("https://github.com/idcesares/GreenRisk/prov/") doc.add_namespace(gr) doc.add_namespace(hf) - agent = doc.agent("gr:GreenRiskPipeline_v0.2", - {"prov:type": "prov:SoftwareAgent", "gr:version": "0.2.0"}) + version = manifest["software_version"] + agent = doc.agent( + f"gr:GreenRiskPipeline_v{version}", + {"prov:type": "prov:SoftwareAgent", "gr:version": version}, + ) # The locked instrument as a first-class entity (rules + MFs, frozen). instrument = doc.entity("gr:instrument_" + manifest["instrument_tag"], - {"prov:type": "gr:FuzzyInstrument", + {"prov:type": "gr:FuzzyInstrument", "gr:tag": manifest["instrument_tag"], - "gr:n_rules": str(len(RULES)), - "gr:defuzz": "Mamdani/centroid"}) + "gr:commit": manifest["instrument_commit"], + "gr:n_rules": str(INSTRUMENT_RULE_COUNT), + "gr:defuzz": INSTRUMENT_METHOD}) # The input dataset. dataset = doc.entity("gr:dataset_tcfd", - {"prov:type": "gr:Corpus", - "gr:name": manifest["dataset"], + {"prov:type": "gr:Corpus", + "gr:name": manifest["dataset"], + "gr:revision": manifest["dataset_revision"], + "gr:sha256": manifest["dataset_train_sha256"], "gr:n_total": str(manifest["n_total"])}) # The run activity (timestamps from the manifest). @@ -83,9 +90,8 @@ def main(): doc.used(run, instrument) # The 5 pinned models, each used by the run. - for short in ["detector"] + [SIGNAL_MAP[v][0] for v in SIGNAL_MAP]: - repo = MODEL_REGISTRY[short]["repo"] - commit = MODEL_REGISTRY[short]["revision"] + for short, commit in manifest["model_revisions"].items(): + repo = manifest["model_repositories"][short] e_model = doc.entity("hf:" + repo, {"prov:type": "gr:PretrainedModel", "gr:short_name": short, @@ -97,7 +103,7 @@ def main(): {"prov:type": "gr:ScoredCorpus", "gr:filename": CSV.name, "gr:n_rows": str(manifest["n_kept"]), - "gr:sha256": csv_hash}) + "gr:sha256": expected_hash}) doc.wasGeneratedBy(results, run) doc.wasDerivedFrom(results, dataset) doc.wasDerivedFrom(results, instrument) diff --git a/scripts/render_provenance_png.py b/scripts/render_provenance_png.py index e9d4c17..c986c51 100644 --- a/scripts/render_provenance_png.py +++ b/scripts/render_provenance_png.py @@ -20,10 +20,10 @@ from matplotlib.lines import Line2D from prov.model import ProvDocument from rdflib import Graph, Literal, URIRef -from rdflib.namespace import RDF, PROV +from rdflib.namespace import PROV, RDF - -GR = "https://greenrisk.ppgi.ufrj.br/prov/" +GR = "https://github.com/idcesares/GreenRisk/prov/" +LEGACY_GR = "https://greenrisk.ppgi.ufrj.br/prov/" HF = "https://huggingface.co/" RELATIONS = { @@ -51,9 +51,10 @@ def parse_provenance(path: Path) -> Graph: def _literal_value(graph: Graph, node: URIRef, predicate_suffix: str) -> str | None: - predicate = URIRef(GR + predicate_suffix) - for value in graph.objects(node, predicate): - return str(value) + for namespace in (GR, LEGACY_GR): + predicate = URIRef(namespace + predicate_suffix) + for value in graph.objects(node, predicate): + return str(value) return None @@ -76,6 +77,8 @@ def _short_id(node: URIRef) -> str: text = str(node) if text.startswith(GR): return text.removeprefix(GR) + if text.startswith(LEGACY_GR): + return text.removeprefix(LEGACY_GR) if text.startswith(HF): return text.removeprefix(HF).split("/")[-1] return text.rsplit("/", 1)[-1].rsplit("#", 1)[-1] diff --git a/scripts/run_contrast_set.py b/scripts/run_contrast_set.py index 59e86a7..13cb0c4 100644 --- a/scripts/run_contrast_set.py +++ b/scripts/run_contrast_set.py @@ -1,9 +1,9 @@ """Score the held-out contrast set through the frozen instrument. -ONE-SHOT held-out evaluation. The instrument (rulebase-locked-v1) has never seen -this set. The pre-registration (stratum + expectation per case) is hardcoded below -and was fixed BEFORE this script was run, so the held-out line is auditable. See -docs/validation.md for the stratification rationale. +Post-lock contrast-set evaluation. The expectations embedded below were not used +to tune the frozen instrument. They and the first public results were committed +together, however, so this is not described as an externally timestamped blind +pre-registration. See docs/validation.md for the protocol and its limitations. Evaluated downstream as: PRIMARY - in-scope greenwashing (vagueness-based) vs rigorous: expect separation @@ -20,23 +20,26 @@ """ import argparse import hashlib +import importlib.metadata import json import pathlib +import platform +import subprocess import sys -from datetime import datetime, timezone +from datetime import UTC, datetime sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) import pandas as pd from datasets import logging as _hf_logging # noqa: F401 (quiet import parity) -from models import score_batch, all_signals_batch, MODEL_REGISTRY -from rule_base import score_paragraph, ANTS +from greenrisk import __version__ +from greenrisk.metadata import INSTRUMENT_COMMIT, INSTRUMENT_TAG, MODEL_REGISTRY +from greenrisk.models import DEVICE, all_signals_batch, score_batch +from greenrisk.rule_base import ANTS, score_paragraph -INSTRUMENT_TAG = "rulebase-locked-v1" - -# --- PRE-REGISTRATION (fixed before scoring; see docs/validation.md) ---------- -# id -> (stratum, expectation). Frozen before scoring; NOT revised after scores. +# --- RECORDED EXPECTATIONS (not used for tuning; see docs/validation.md) ------ +# id -> (stratum, expectation). Do not revise these historical expectations. PREREG = { "GW-001": ("in_scope", "elevated"), "GW-002": ("in_scope", "elevated"), @@ -56,15 +59,52 @@ } -def sha12(text: str) -> str: - return hashlib.sha256(text.encode()).hexdigest()[:12] +def sha256_text(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def sha256_file(path: pathlib.Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def git_state(root: pathlib.Path) -> dict[str, str | bool]: + """Return the exact source revision when running from a Git checkout.""" + try: + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=root, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + dirty = bool( + subprocess.run( + ["git", "status", "--porcelain"], + cwd=root, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + ) + return {"commit": commit, "dirty": dirty} + except (OSError, subprocess.CalledProcessError): + return {"commit": "unavailable", "dirty": "unavailable"} def main(gate: float, batch_size: int): root = pathlib.Path(__file__).resolve().parents[1] - df = pd.read_csv(root / "data" / "contrast_set.csv") + dataset_path = root / "data" / "contrast_set.csv" + df = pd.read_csv(dataset_path) + expected_ids = set(PREREG) + received_ids = set(df["id"]) + if received_ids != expected_ids: + raise ValueError( + "contrast-set IDs do not match the frozen protocol: " + f"missing={sorted(expected_ids - received_ids)}, " + f"unexpected={sorted(received_ids - expected_ids)}" + ) texts = df["paragraph"].tolist() - print(f"contrast set: {len(texts)} paragraphs (held-out, first instrument contact)") + print(f"contrast set: {len(texts)} paragraphs (post-lock evaluation)") # Stage 1 - climate gate (recorded, not used to drop) climate = [r["yes"] for r in score_batch("detector", texts, batch_size=batch_size)] @@ -77,48 +117,69 @@ def main(gate: float, batch_size: int): for i, t in enumerate(texts): sig = {v: cols[v][i] for v in ANTS} risk_score, trace = score_paragraph(sig) - stratum, expect = PREREG.get(df["id"][i], ("?", "?")) + stratum, expect = PREREG[df["id"][i]] rows.append({ "id": df["id"][i], "group": df["group"][i], "stratum": stratum, "expectation": expect, "company": df["source_company"][i], - "climate_p": round(climate[i], 4), + "climate_p": climate[i], "gate_pass": bool(climate[i] >= gate), - "specificity": round(sig["specificity"], 4), - "commitment": round(sig["commitment"], 4), - "sentiment_asymmetry": round(sig["sentiment_asymmetry"], 4), - "netzero": round(sig["netzero"], 4), + "specificity": sig["specificity"], + "commitment": sig["commitment"], + "sentiment_asymmetry": sig["sentiment_asymmetry"], + "netzero": sig["netzero"], "risk": risk_score, "top_rule": trace[0]["rule"] if trace else "", - "fired_rules": "|".join(f"{x['rule']}:{x['fire']:.2f}" for x in trace), - "sha12": sha12(t), + "fired_rules": json.dumps(trace, separators=(",", ":")), + "text_sha256": sha256_text(t), }) out = pd.DataFrame(rows) out_dir = root / "artifacts" / "contrast_run" out_dir.mkdir(parents=True, exist_ok=True) - out.to_csv(out_dir / "contrast_scored.csv", index=False) + csv_path = out_dir / "contrast_scored.csv" + out.to_csv(csv_path, index=False, lineterminator="\n") manifest = { + "schema_version": "2.0", + "software_version": __version__, + "source": git_state(root), "instrument_tag": INSTRUMENT_TAG, - "dataset": "data/contrast_set.csv (held-out)", + "instrument_commit": INSTRUMENT_COMMIT, + "dataset": "data/contrast_set.csv", + "dataset_sha256": sha256_file(dataset_path), "gate": gate, "batch_size": batch_size, "n": len(texts), "n_gate_pass": int(out["gate_pass"].sum()), - "prereg_ratified": "2026-06-17", - "model_hashes": {k: v["revision"] for k, v in MODEL_REGISTRY.items()}, - "timestamp_utc": datetime.now(timezone.utc).isoformat(), + "protocol_status": ( + "expectations recorded in the same public commit as first results; " + "not an externally timestamped preregistration" + ), + "expectations_recorded": "2026-06-17", + "model_repositories": {k: v["repo"] for k, v in MODEL_REGISTRY.items()}, + "model_revisions": {k: v["revision"] for k, v in MODEL_REGISTRY.items()}, + "environment": { + "python": platform.python_version(), + "device": DEVICE, + "torch": importlib.metadata.version("torch"), + "transformers": importlib.metadata.version("transformers"), + }, + "uv_lock_sha256": sha256_file(root / "uv.lock"), + "outputs": {csv_path.name: {"sha256": sha256_file(csv_path), "rows": len(out)}}, + "timestamp_utc": datetime.now(UTC).isoformat(), } - (out_dir / "run_manifest.json").write_text(json.dumps(manifest, indent=2)) + (out_dir / "run_manifest.json").write_text( + json.dumps(manifest, indent=2) + "\n", encoding="utf-8", newline="\n" + ) print(f"\nwrote {out_dir / 'contrast_scored.csv'} ({len(out)} rows)") print(f"gate>={gate}: {int(out['gate_pass'].sum())}/{len(out)} passed\n") print("risk by stratum:") print(out.groupby("stratum")["risk"].agg(["count", "mean", "min", "max"]).round(2).to_string()) - print("\nper-case (pre-registration vs. observed):") + print("\nper-case (recorded expectation vs. observed):") cols_show = ["id", "stratum", "expectation", "gate_pass", "specificity", "commitment", "netzero", "risk", "top_rule"] print(out[cols_show].to_string(index=False)) @@ -129,4 +190,4 @@ def main(gate: float, batch_size: int): ap.add_argument("--gate", type=float, default=0.5) ap.add_argument("--batch-size", type=int, default=32) a = ap.parse_args() - main(a.gate, a.batch_size) \ No newline at end of file + main(a.gate, a.batch_size) diff --git a/scripts/run_full_corpus.py b/scripts/run_full_corpus.py index 488c91b..6317a4a 100644 --- a/scripts/run_full_corpus.py +++ b/scripts/run_full_corpus.py @@ -13,10 +13,13 @@ """ import argparse import hashlib +import importlib.metadata import json import pathlib +import platform +import subprocess import sys -from datetime import datetime, timezone +from datetime import UTC, datetime # Repo root on the path so core modules import when run from anywhere. sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) @@ -24,18 +27,54 @@ import pandas as pd from datasets import load_dataset -from models import score_batch, all_signals_batch, MODEL_REGISTRY -from rule_base import score_paragraph, ANTS - -INSTRUMENT_TAG = "rulebase-locked-v1" - - -def sha12(text: str) -> str: - return hashlib.sha256(text.encode()).hexdigest()[:12] +from greenrisk import __version__ +from greenrisk.metadata import ( + INSTRUMENT_COMMIT, + INSTRUMENT_TAG, + MODEL_REGISTRY, + TCFD_DATASET_ID, + TCFD_DATASET_REVISION, + TCFD_TRAIN_SHA256, +) +from greenrisk.models import DEVICE, all_signals_batch, score_batch +from greenrisk.rule_base import ANTS, score_paragraph + + +def sha256_text(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def sha256_file(path: pathlib.Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def git_state(root: pathlib.Path) -> dict[str, str | bool]: + """Return the exact source revision when running from a Git checkout.""" + try: + commit = subprocess.run( + ["git", "rev-parse", "HEAD"], + cwd=root, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + dirty = bool( + subprocess.run( + ["git", "status", "--porcelain"], + cwd=root, + check=True, + capture_output=True, + text=True, + ).stdout.strip() + ) + return {"commit": commit, "dirty": dirty} + except (OSError, subprocess.CalledProcessError): + return {"commit": "unavailable", "dirty": "unavailable"} def main(gate: float, batch_size: int): - ds = load_dataset("climatebert/tcfd_recommendations")["train"] + root = pathlib.Path(__file__).resolve().parents[1] + ds = load_dataset(TCFD_DATASET_ID, revision=TCFD_DATASET_REVISION)["train"] texts = list(ds["text"]) categories = [ds.features["label"].names[i] for i in ds["label"]] print(f"corpus: {len(texts)} paragraphs") @@ -56,42 +95,56 @@ def main(gate: float, batch_size: int): for j, i in enumerate(keep_idx): sig = {v: cols[v][j] for v in ANTS} risk_score, trace = score_paragraph(sig) - trace_str = "|".join(f"{t['rule']}:{t['fire']:.2f}" for t in trace) rows.append({ "row": i, - "sha12": sha12(texts[i]), + "text_sha256": sha256_text(texts[i]), "tcfd_category": categories[i], - "climate_p": round(climate[i], 4), - "specificity": round(sig["specificity"], 4), - "commitment": round(sig["commitment"], 4), - "sentiment_asymmetry": round(sig["sentiment_asymmetry"], 4), - "netzero": round(sig["netzero"], 4), + "climate_p": climate[i], + "specificity": sig["specificity"], + "commitment": sig["commitment"], + "sentiment_asymmetry": sig["sentiment_asymmetry"], + "netzero": sig["netzero"], "risk": risk_score, "n_fired": len(trace), "top_rule": trace[0]["rule"] if trace else "", "top_fire": trace[0]["fire"] if trace else 0.0, - "fired_rules": trace_str, + "fired_rules": json.dumps(trace, separators=(",", ":")), "text": texts[i], }) df = pd.DataFrame(rows) - out_dir = pathlib.Path(__file__).resolve().parents[1] / "artifacts" / "corpus_run" + out_dir = root / "artifacts" / "corpus_run" out_dir.mkdir(parents=True, exist_ok=True) csv_path = out_dir / "tcfd_scored.csv" - df.to_csv(csv_path, index=False) + df.to_csv(csv_path, index=False, lineterminator="\n") manifest = { + "schema_version": "2.0", + "software_version": __version__, + "source": git_state(root), "instrument_tag": INSTRUMENT_TAG, - "dataset": "climatebert/tcfd_recommendations", + "instrument_commit": INSTRUMENT_COMMIT, + "dataset": TCFD_DATASET_ID, + "dataset_revision": TCFD_DATASET_REVISION, + "dataset_train_sha256": TCFD_TRAIN_SHA256, "gate": gate, "batch_size": batch_size, "n_total": len(texts), "n_kept": len(keep_idx), - "model_hashes": {k: v["revision"] for k, v in MODEL_REGISTRY.items()}, - "timestamp_utc": datetime.now(timezone.utc).isoformat(), + "model_repositories": {k: v["repo"] for k, v in MODEL_REGISTRY.items()}, + "model_revisions": {k: v["revision"] for k, v in MODEL_REGISTRY.items()}, + "environment": { + "python": platform.python_version(), + "device": DEVICE, + "torch": importlib.metadata.version("torch"), + "transformers": importlib.metadata.version("transformers"), + }, + "uv_lock_sha256": sha256_file(root / "uv.lock"), + "outputs": {csv_path.name: {"sha256": sha256_file(csv_path), "rows": len(df)}}, + "timestamp_utc": datetime.now(UTC).isoformat(), } manifest_path = out_dir / "run_manifest.json" - manifest_path.write_text(json.dumps(manifest, indent=2)) + manifest_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8", newline="\n") # Summary print(f"\nwrote {csv_path} ({len(df)} rows)") diff --git a/scripts/sanity_check_distributions.py b/scripts/sanity_check_distributions.py index 9011a50..20b72d7 100644 --- a/scripts/sanity_check_distributions.py +++ b/scripts/sanity_check_distributions.py @@ -7,26 +7,25 @@ """ import pathlib -import sys - -# Repo root on the path so core modules import when run from anywhere. -ROOT = pathlib.Path(__file__).resolve().parents[1] -sys.path.insert(0, str(ROOT)) -FIG_DIR = ROOT / "artifacts" / "figures" -FIG_DIR.mkdir(parents=True, exist_ok=True) import matplotlib + matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from datasets import load_dataset -import models -from models import all_signals_batch, SIGNAL_MAP +from greenrisk import models +from greenrisk.metadata import TCFD_DATASET_ID, TCFD_DATASET_REVISION +from greenrisk.models import all_signals_batch + +ROOT = pathlib.Path(__file__).resolve().parents[1] +FIG_DIR = ROOT / "artifacts" / "figures" +FIG_DIR.mkdir(parents=True, exist_ok=True) N = 200 -ds = load_dataset("climatebert/tcfd_recommendations") +ds = load_dataset(TCFD_DATASET_ID, revision=TCFD_DATASET_REVISION) sample = ds["test"]["text"][:N] print(f"Scoring {len(sample)} TCFD paragraphs x 4 models on {models.DEVICE} (batched)...") @@ -51,4 +50,4 @@ high = (values > 0.6).mean() print(f"\n{var}: saved dist_{var}.png") print(f" mean={values.mean():.3f} median={np.median(values):.3f}") - print(f" Low(<0.4)={low:.0%} Medium(0.4-0.6)={med:.0%} High(>0.6)={high:.0%}") \ No newline at end of file + print(f" Low(<0.4)={low:.0%} Medium(0.4-0.6)={med:.0%} High(>0.6)={high:.0%}") diff --git a/scripts/validation/anchor_verify.py b/scripts/validation/anchor_verify.py index 971d9db..c9be010 100644 --- a/scripts/validation/anchor_verify.py +++ b/scripts/validation/anchor_verify.py @@ -17,7 +17,7 @@ sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[2])) -from rule_base import score_paragraph, format_trace +from greenrisk.rule_base import format_trace, score_paragraph # Pre-registered profile + expectation. # sentiment is Low and inert here (the net-zero/spine rules cap the score); set diff --git a/scripts/validation/hash3_characterize.py b/scripts/validation/hash3_characterize.py index e5b3bdd..f97d20c 100644 --- a/scripts/validation/hash3_characterize.py +++ b/scripts/validation/hash3_characterize.py @@ -20,9 +20,10 @@ import skfuzzy as fuzz from datasets import load_dataset -from models import all_signals_batch, score_batch -from rule_base import score_paragraph, ANTS -from linguistic_variables import risk +from greenrisk.linguistic_variables import risk +from greenrisk.metadata import TCFD_DATASET_ID, TCFD_DATASET_REVISION +from greenrisk.models import all_signals_batch, score_batch +from greenrisk.rule_base import ANTS, score_paragraph COMMIT_HI = 0.90 # commitment model firing strong SPEC_LO = 0.40 # vague (specificity Low region) @@ -39,11 +40,13 @@ def ascii_safe(s, n=300): def main(n, gate, show): - ds = load_dataset("climatebert/tcfd_recommendations")["train"] + ds = load_dataset(TCFD_DATASET_ID, revision=TCFD_DATASET_REVISION)["train"] texts = ds["text"][:n] climate = [r["yes"] for r in score_batch("detector", texts)] - kept = [(i, t) for i, (t, c) in enumerate(zip(texts, climate)) if c >= gate] + kept = [ + (i, t) for i, (t, c) in enumerate(zip(texts, climate, strict=True)) if c >= gate + ] keep_texts = [t for _, t in kept] print(f"gate>={gate}: kept {len(kept)}/{len(texts)} climate paras") @@ -53,7 +56,7 @@ def main(n, gate, show): commit_hi = 0 flagged = [] # the #3 signature: high commitment AND vague band_counts = {b: 0 for b in BANDS} - for (orig_i, text), sig in zip(kept, signals): + for (orig_i, text), sig in zip(kept, signals, strict=True): if sig["commitment"] >= COMMIT_HI: commit_hi += 1 if sig["specificity"] <= SPEC_LO: @@ -66,7 +69,7 @@ def main(n, gate, show): n_kept = len(kept) print(f"\ncommitment >= {COMMIT_HI:.2f} (model fires strong) : {commit_hi}/{n_kept}") print(f" AND specificity <= {SPEC_LO:.2f} (the #3 signature) : {len(flagged)}/{n_kept}") - print(f" risk bands among the signature: " + print(" risk bands among the signature: " + ", ".join(f"{b}={band_counts[b]}" for b in BANDS)) flagged.sort(key=lambda r: r[3], reverse=True) diff --git a/scripts/validation/integration_seam_test.py b/scripts/validation/integration_seam_test.py index 0d0c49c..b09bbc4 100644 --- a/scripts/validation/integration_seam_test.py +++ b/scripts/validation/integration_seam_test.py @@ -16,27 +16,29 @@ import hashlib import pathlib import sys -from datetime import datetime, timezone +from datetime import UTC, datetime # Repo root on the path so core modules import when run from anywhere. sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[2])) from datasets import load_dataset -from prov.model import ProvDocument, Namespace +from prov.model import Namespace, ProvDocument -from models import all_signals_batch, score_batch, SIGNAL_MAP, MODEL_REGISTRY -from rule_base import score_paragraph, library_firings, ANTS, RULES, _term_membership +from greenrisk import __version__ +from greenrisk.metadata import TCFD_DATASET_ID, TCFD_DATASET_REVISION +from greenrisk.models import MODEL_REGISTRY, SIGNAL_MAP, all_signals_batch, score_batch +from greenrisk.rule_base import ANTS, RULES, _term_membership, library_firings, score_paragraph FIRING_TOL = 1e-9 # library vs. re-derived firing must agree to float precision MAP_TOL = 1e-9 # in-flow sentiment signal vs. independent P(opportunity) read -def sha12(text: str) -> str: - return hashlib.sha256(text.encode()).hexdigest()[:12] +def text_sha256(text: str) -> str: + return hashlib.sha256(text.encode()).hexdigest() def main(n: int, gate: float): - ds = load_dataset("climatebert/tcfd_recommendations")["train"] + ds = load_dataset(TCFD_DATASET_ID, revision=TCFD_DATASET_REVISION)["train"] texts = ds["text"][:n] # --- SEAM A: type/key match (assert, don't assume) --------------------- @@ -47,9 +49,11 @@ def main(n: int, gate: float): # --- Stage 1: climate gate (one batched detector pass) ----------------- climate = [r["yes"] for r in score_batch("detector", texts)] - kept = [(i, t) for i, (t, c) in enumerate(zip(texts, climate)) if c >= gate] + kept = [ + (i, t) for i, (t, c) in enumerate(zip(texts, climate, strict=True)) if c >= gate + ] keep_texts = [t for _, t in kept] - gate_hashes = [sha12(t) for t in keep_texts] + gate_hashes = [text_sha256(t) for t in keep_texts] print(f"gate>={gate}: kept {len(kept)}/{len(texts)} " f"({len(texts) - len(kept)} non-climate dropped)") @@ -71,7 +75,7 @@ def main(n: int, gate: float): # SEAM B: identity preservation — the paragraph scored here is the same # one that entered the gate at this position. Caching / reordering is # exactly where identity quietly breaks. - assert sha12(text) == gate_hashes[j], f"identity broken at row {orig_i}" + assert text_sha256(text) == gate_hashes[j], f"identity broken at row {orig_i}" # SEAM C: the sentiment signal is the MAPPED value (P('opportunity')), # not a raw class. It must equal the sentiment model's opportunity @@ -98,7 +102,7 @@ def main(n: int, gate: float): print(f"SEAMS OK on {len(records)} paragraphs:") print(f" A key-match : {sorted(ANTS)} == scorer keys") print(f" B identity : all {len(records)} hashes preserved") - print(f" C sentiment map : in-flow signal == P(opportunity), in [0,1]") + print(" C sentiment map : in-flow signal == P(opportunity), in [0,1]") print(f" defensibility : max |library - rederived| firing = " f"{max_firing_disc:.2e}") @@ -117,22 +121,25 @@ def emit_run_provenance(records, gate): """ rec = records[0] # exemplar = first kept paragraph text, sig, score_val = rec["text"], rec["sig"], rec["score"] - h = sha12(text) + h = text_sha256(text) doc = ProvDocument() - gr = Namespace("gr", "https://greenrisk.ppgi.ufrj.br/prov/") + gr = Namespace("gr", "https://github.com/idcesares/GreenRisk/prov/") hf = Namespace("hf", "https://huggingface.co/") - doc.set_default_namespace("https://greenrisk.ppgi.ufrj.br/prov/") + doc.set_default_namespace("https://github.com/idcesares/GreenRisk/prov/") doc.add_namespace(gr) doc.add_namespace(hf) - agent = doc.agent("gr:GreenRiskPipeline_v0.2", - {"prov:type": "prov:SoftwareAgent", "gr:version": "0.2.0"}) - now = datetime.now(timezone.utc) + agent = doc.agent( + f"gr:GreenRiskPipeline_v{__version__}", + {"prov:type": "prov:SoftwareAgent", "gr:version": __version__}, + ) + now = datetime.now(UTC) run = doc.activity("gr:seam_run_" + h, now, now, {"prov:type": "gr:Phase4SeamRun", "gr:n_paragraphs": str(len(records)), - "gr:gate": str(gate), "gr:dataset": "climatebert/tcfd_recommendations"}) + "gr:gate": str(gate), "gr:dataset": TCFD_DATASET_ID, + "gr:dataset_revision": TCFD_DATASET_REVISION}) doc.wasAssociatedWith(run, agent) paragraph = doc.entity("gr:paragraph_" + h, @@ -151,7 +158,7 @@ def emit_run_provenance(records, gate): {"prov:type": "gr:ModelInference", "gr:reads_label": label}) e_sig = doc.entity("gr:signal_%s_%s" % (h, var), {"prov:type": "gr:Signal", "gr:variable": var, - "gr:value": str(round(sig[var], 6))}) + "gr:value": str(sig[var])}) doc.used(a_inf, paragraph) doc.used(a_inf, e_model) doc.wasGeneratedBy(e_sig, a_inf) diff --git a/scripts/validation/mf_experiment.py b/scripts/validation/mf_experiment.py index 56f6623..b3e2970 100644 --- a/scripts/validation/mf_experiment.py +++ b/scripts/validation/mf_experiment.py @@ -26,11 +26,17 @@ from datasets import load_dataset from skfuzzy import control as ctrl -from models import all_signals_batch, score_batch -from rule_base import RULES, RISK, ANTS as TRI_ANTS, score_paragraph -from linguistic_variables import ( - specificity_trap, commitment, sentiment_asymmetry, netzero, risk, +from greenrisk.linguistic_variables import ( + commitment, + netzero, + risk, + sentiment_asymmetry, + specificity_trap, ) +from greenrisk.metadata import TCFD_DATASET_ID, TCFD_DATASET_REVISION +from greenrisk.models import all_signals_batch, score_batch +from greenrisk.rule_base import ANTS as TRI_ANTS +from greenrisk.rule_base import RISK, RULES, score_paragraph # Trapezoidal antecedent set: specificity swapped, the other three unchanged. TRAP_ANTS = { @@ -69,11 +75,11 @@ def band(score): def main(n, gate): - ds = load_dataset("climatebert/tcfd_recommendations")["train"] + ds = load_dataset(TCFD_DATASET_ID, revision=TCFD_DATASET_REVISION)["train"] texts = ds["text"][:n] climate = [r["yes"] for r in score_batch("detector", texts)] - kept_texts = [t for t, c in zip(texts, climate) if c >= gate] + kept_texts = [t for t, c in zip(texts, climate, strict=True) if c >= gate] print(f"gate>={gate}: {len(kept_texts)}/{len(texts)} climate paras\n") cols = all_signals_batch(kept_texts) @@ -89,8 +95,10 @@ def main(n, gate): s_tri, _ = score_paragraph(sig) # locked triangular instrument s_trap = score_with(trap_system, TRAP_ANTS, sig) b_tri, b_trap = band(s_tri), band(s_trap) - tri_scores.append(s_tri); trap_scores.append(s_trap) - tri_bands[b_tri] += 1; trap_bands[b_trap] += 1 + tri_scores.append(s_tri) + trap_scores.append(s_trap) + tri_bands[b_tri] += 1 + trap_bands[b_trap] += 1 if b_tri != b_trap: crossings.append((j, sig["specificity"], s_tri, s_trap, b_tri, b_trap)) diff --git a/scripts/verify_artifact_integrity.py b/scripts/verify_artifact_integrity.py new file mode 100644 index 0000000..5f0e008 --- /dev/null +++ b/scripts/verify_artifact_integrity.py @@ -0,0 +1,80 @@ +"""Verify committed result files against manifests and PROV-O records.""" + +from __future__ import annotations + +import csv +import hashlib +import json +from pathlib import Path + +from greenrisk.metadata import INSTRUMENT_COMMIT, INSTRUMENT_TAG, MODEL_REGISTRY + +ROOT = Path(__file__).resolve().parents[1] + + +def sha256_file(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1 << 20), b""): + digest.update(chunk) + return digest.hexdigest() + + +def row_count(path: Path) -> int: + with path.open(encoding="utf-8", newline="") as stream: + return sum(1 for _ in csv.reader(stream)) - 1 + + +def verify_run(run_name: str, provenance_stem: str) -> None: + run_dir = ROOT / "artifacts" / run_name + manifest_path = run_dir / "run_manifest.json" + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + + assert manifest["schema_version"] == "2.0" + assert manifest["instrument_tag"] == INSTRUMENT_TAG + assert manifest["instrument_commit"] == INSTRUMENT_COMMIT + assert manifest["model_repositories"] == { + name: value["repo"] for name, value in MODEL_REGISTRY.items() + } + assert manifest["model_revisions"] == { + name: value["revision"] for name, value in MODEL_REGISTRY.items() + } + + if "dataset_sha256" in manifest: + dataset_path = ROOT / manifest["dataset"] + actual = sha256_file(dataset_path) + assert actual == manifest["dataset_sha256"], ( + f"{dataset_path}: expected {manifest['dataset_sha256']}, got {actual}" + ) + + provenance_paths = [ + ROOT / "artifacts" / "provenance" / f"{provenance_stem}.ttl", + ROOT / "artifacts" / "provenance" / f"{provenance_stem}.json", + ] + provenance_text = [path.read_text(encoding="utf-8") for path in provenance_paths] + + for filename, record in manifest["outputs"].items(): + output_path = run_dir / filename + actual = sha256_file(output_path) + assert actual == record["sha256"], ( + f"{output_path}: expected {record['sha256']}, got {actual}" + ) + if "rows" in record: + actual_rows = row_count(output_path) + assert actual_rows == record["rows"], ( + f"{output_path}: expected {record['rows']} rows, got {actual_rows}" + ) + for provenance_path, text in zip(provenance_paths, provenance_text, strict=True): + assert actual in text, f"{provenance_path} does not bind {filename} to {actual}" + + print(f"verified {run_name}: {len(manifest['outputs'])} output(s)") + + +def main() -> None: + verify_run("corpus_run", "corpus_run") + verify_run("contrast_run", "contrast_run") + print("artifact manifests and provenance records are internally consistent") + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_model_revisions.py b/scripts/verify_model_revisions.py new file mode 100644 index 0000000..39959af --- /dev/null +++ b/scripts/verify_model_revisions.py @@ -0,0 +1,36 @@ +"""Check whether pinned model commits still resolve and report upstream HEADs. + +This command is deliberately read-only. Updating a revision would change the +frozen instrument and therefore requires a separately logged ablation. +""" + +from __future__ import annotations + +import json + +from huggingface_hub import HfApi + +from greenrisk.metadata import MODEL_REGISTRY + + +def main() -> None: + api = HfApi() + report = {} + for name, coordinates in MODEL_REGISTRY.items(): + pinned = api.model_info(coordinates["repo"], revision=coordinates["revision"]) + current = api.model_info(coordinates["repo"]) + report[name] = { + "repo": coordinates["repo"], + "pinned_revision": coordinates["revision"], + "pinned_resolves_to": pinned.sha, + "upstream_head": current.sha, + "upstream_changed": current.sha != coordinates["revision"], + } + if pinned.sha != coordinates["revision"]: + raise RuntimeError(f"{name}: pinned revision did not resolve exactly") + + print(json.dumps(report, indent=2, sort_keys=True)) + + +if __name__ == "__main__": + main() diff --git a/src/greenrisk/__init__.py b/src/greenrisk/__init__.py new file mode 100644 index 0000000..8180442 --- /dev/null +++ b/src/greenrisk/__init__.py @@ -0,0 +1,14 @@ +"""GreenRisk: explainable-by-construction greenwashing-risk scoring.""" + +__version__ = "0.2.0" + +__all__ = ["__version__", "format_trace", "score_paragraph"] + + +def __getattr__(name: str): + """Load the fuzzy scorer lazily so metadata remains dependency-light.""" + if name in {"format_trace", "score_paragraph"}: + from . import rule_base + + return getattr(rule_base, name) + raise AttributeError(name) diff --git a/src/greenrisk/cli.py b/src/greenrisk/cli.py new file mode 100644 index 0000000..34a6805 --- /dev/null +++ b/src/greenrisk/cli.py @@ -0,0 +1,152 @@ +"""Command-line interfaces for signal-level and raw-text GreenRisk scoring.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path + +from . import __version__ +from .metadata import INSTRUMENT_COMMIT, INSTRUMENT_TAG +from .rule_base import ANTS, format_trace, score_paragraph + + +def probability(value: str) -> float: + parsed = float(value) + if not 0.0 <= parsed <= 1.0: + raise argparse.ArgumentTypeError("expected a probability in [0, 1]") + return parsed + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="greenrisk", + description="Score climate disclosures with the locked GreenRisk instrument.", + ) + parser.add_argument("--version", action="version", version=f"%(prog)s {__version__}") + subparsers = parser.add_subparsers(dest="command", required=True) + + signals = subparsers.add_parser( + "score-signals", help="score four already-computed signal probabilities" + ) + signals.add_argument("--specificity", required=True, type=probability) + signals.add_argument("--commitment", required=True, type=probability) + signals.add_argument( + "--sentiment-asymmetry", + dest="sentiment_asymmetry", + required=True, + type=probability, + ) + signals.add_argument("--netzero", required=True, type=probability) + signals.add_argument("--json", action="store_true", help="emit structured JSON") + + text = subparsers.add_parser( + "score-text", help="run the climate gate, four pinned models, and fuzzy scorer" + ) + source = text.add_mutually_exclusive_group(required=True) + source.add_argument("--text", help="paragraph text to score") + source.add_argument("--file", type=Path, help="UTF-8 text file to score") + text.add_argument("--gate", default=0.5, type=probability) + text.add_argument( + "--force", + action="store_true", + help="score text below the climate-relevance gate and mark it as forced", + ) + text.add_argument("--json", action="store_true", help="emit structured JSON") + text.add_argument( + "--provenance", + type=Path, + help="write per-score W3C PROV-O as .ttl or .json (scored text only)", + ) + return parser + + +def _score_signals(args: argparse.Namespace) -> dict: + signals = {name: getattr(args, name) for name in ANTS} + risk, trace = score_paragraph(signals) + return { + "schema_version": "1.0", + "software_version": __version__, + "instrument": {"tag": INSTRUMENT_TAG, "commit": INSTRUMENT_COMMIT}, + "mode": "signals", + "status": "scored", + "signals": signals, + "risk": risk, + "trace": trace, + } + + +def _read_text(args: argparse.Namespace) -> str: + value = args.text if args.text is not None else args.file.read_text(encoding="utf-8") + if not value.strip(): + raise ValueError("input text must not be empty") + return value + + +def _score_text(args: argparse.Namespace) -> dict: + from .models import all_signals, is_climate + + text = _read_text(args) + climate_probability = is_climate(text) + passed = climate_probability >= args.gate + payload = { + "schema_version": "1.0", + "software_version": __version__, + "instrument": {"tag": INSTRUMENT_TAG, "commit": INSTRUMENT_COMMIT}, + "mode": "text", + "input": { + "sha256": hashlib.sha256(text.encode("utf-8")).hexdigest(), + "char_count": len(text), + }, + "gate": { + "probability": climate_probability, + "threshold": args.gate, + "passed": passed, + "forced": bool(args.force and not passed), + }, + } + if not passed and not args.force: + payload.update({"status": "not_scored", "signals": None, "risk": None, "trace": []}) + return payload + + signals = all_signals(text) + risk, trace = score_paragraph(signals) + payload.update( + {"status": "scored", "signals": signals, "risk": risk, "trace": trace} + ) + return payload + + +def _print_payload(payload: dict, as_json: bool) -> None: + if as_json: + print(json.dumps(payload, indent=2, sort_keys=True)) + return + if payload["status"] != "scored": + gate = payload["gate"] + print( + f"NOT SCORED: climate probability {gate['probability']:.3f} " + f"is below gate {gate['threshold']:.3f}" + ) + return + print(format_trace(payload["signals"], payload["risk"], payload["trace"])) + + +def main() -> None: + parser = build_parser() + args = parser.parse_args() + try: + payload = _score_signals(args) if args.command == "score-signals" else _score_text(args) + if getattr(args, "provenance", None): + if payload["status"] != "scored": + parser.error("--provenance requires a scored paragraph; use --force if appropriate") + from .provenance import write_score_provenance + + write_score_provenance(payload, args.provenance) + _print_payload(payload, args.json) + except (OSError, TypeError, ValueError) as exc: + parser.error(str(exc)) + + +if __name__ == "__main__": + main() diff --git a/linguistic_variables.py b/src/greenrisk/linguistic_variables.py similarity index 99% rename from linguistic_variables.py rename to src/greenrisk/linguistic_variables.py index a753beb..dc036f7 100644 --- a/linguistic_variables.py +++ b/src/greenrisk/linguistic_variables.py @@ -53,4 +53,4 @@ specificity_trap = ctrl.Antecedent(universe_input, "specificity_trap") specificity_trap["Low"] = fuzz.trapmf(specificity_trap.universe, [0, 0, 0.15, 0.4]) specificity_trap["Medium"] = fuzz.trimf(specificity_trap.universe, [0.2, 0.5, 0.8]) -specificity_trap["High"] = fuzz.trimf(specificity_trap.universe, [0.6, 1, 1]) \ No newline at end of file +specificity_trap["High"] = fuzz.trimf(specificity_trap.universe, [0.6, 1, 1]) diff --git a/src/greenrisk/metadata.py b/src/greenrisk/metadata.py new file mode 100644 index 0000000..fd5d9a1 --- /dev/null +++ b/src/greenrisk/metadata.py @@ -0,0 +1,45 @@ +"""Immutable identifiers used by the locked GreenRisk research instrument.""" + +INSTRUMENT_TAG = "rulebase-locked-v1" +INSTRUMENT_COMMIT = "a40288ac8eacfe0de1987884ec03bea9457972c5" +INSTRUMENT_RULE_COUNT = 17 +INSTRUMENT_METHOD = "Mamdani/centroid" + +TCFD_DATASET_ID = "climatebert/tcfd_recommendations" +TCFD_DATASET_REVISION = "aee5c98f0bf7835bfb08308ffc7c17216657976b" +TCFD_TRAIN_SHA256 = "1f16bb38a021aeafad6935eb5aa3f072c2e51b3dcf01eaee88a32ef537029f1f" + +# Frozen upstream model coordinates. Keeping them in this dependency-free +# module lets manifests and integrity tests inspect the instrument without +# importing PyTorch or downloading model weights. +MODEL_REGISTRY = { + "detector": { + "repo": "climatebert/distilroberta-base-climate-detector", + "revision": "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c", + }, + "specificity": { + "repo": "climatebert/distilroberta-base-climate-specificity", + "revision": "4ada96ed4bf5c3a7a711282e41f1ab9b29f0ddea", + }, + "commitment": { + "repo": "climatebert/distilroberta-base-climate-commitment", + "revision": "17337c3292df16a8fe93b1505dfe4122d50a4c91", + }, + "sentiment": { + "repo": "climatebert/distilroberta-base-climate-sentiment", + "revision": "e9f9a94ee4263f5ad5cfc97b8539a497fc88aa7d", + }, + "netzero": { + "repo": "climatebert/netzero-reduction", + "revision": "25cf57e30613a2156fee1fe3f917036df4a5c0d1", + }, +} + +# Fuzzy input -> (model short name, probability label). These mappings are part +# of the locked instrument and must not change without a logged ablation. +SIGNAL_MAP = { + "specificity": ("specificity", "spec"), + "commitment": ("commitment", "yes"), + "sentiment_asymmetry": ("sentiment", "opportunity"), + "netzero": ("netzero", "net-zero"), +} diff --git a/models.py b/src/greenrisk/models.py similarity index 76% rename from models.py rename to src/greenrisk/models.py index c80a03c..021bfae 100644 --- a/models.py +++ b/src/greenrisk/models.py @@ -1,43 +1,16 @@ -"""GREENRISK ClimateBERT model registry. Hashes pinned for reproducibility.""" +"""GreenRisk ClimateBERT model registry and inference adapters.""" # Use the GPU if a CUDA-enabled torch sees one; otherwise CPU. Auto-detected. import os -from dotenv import load_dotenv + import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer -from huggingface_hub import login -load_dotenv() -token = os.environ.get("HF_TOKEN") -if token: - login(token=token) # only for gated/private models; safe no-op without a token +from .metadata import MODEL_REGISTRY, SIGNAL_MAP DEVICE = "cuda" if torch.cuda.is_available() else "cpu" -MODEL_REGISTRY = { - "detector": { - "repo": "climatebert/distilroberta-base-climate-detector", - "revision": "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c", - }, - "specificity": { - "repo": "climatebert/distilroberta-base-climate-specificity", - "revision": "4ada96ed4bf5c3a7a711282e41f1ab9b29f0ddea", - }, - "commitment": { - "repo": "climatebert/distilroberta-base-climate-commitment", - "revision": "17337c3292df16a8fe93b1505dfe4122d50a4c91", - }, - "sentiment": { - "repo": "climatebert/distilroberta-base-climate-sentiment", - "revision": "e9f9a94ee4263f5ad5cfc97b8539a497fc88aa7d", - }, - "netzero": { - "repo": "climatebert/netzero-reduction", - "revision": "25cf57e30613a2156fee1fe3f917036df4a5c0d1", - }, -} - # --------------------------------------------------------------------------- # Model loading # --------------------------------------------------------------------------- @@ -47,11 +20,15 @@ def load(name: str): if name not in MODEL_REGISTRY: raise KeyError(f"Unknown model: {name}. Known: {list(MODEL_REGISTRY)}") spec = MODEL_REGISTRY[name] + # These models are public. If a token is present, pass it to the individual + # requests without invoking huggingface_hub.login() or persisting credentials + # as an import-time side effect. + token = os.environ.get("HF_TOKEN") or None model = AutoModelForSequenceClassification.from_pretrained( - spec["repo"], revision=spec["revision"] + spec["repo"], revision=spec["revision"], token=token ) tokenizer = AutoTokenizer.from_pretrained( - spec["repo"], revision=spec["revision"] + spec["repo"], revision=spec["revision"], token=token ) return model, tokenizer @@ -75,12 +52,21 @@ def _get(name: str): return _MODELS[name] +def clear_model_cache() -> None: + """Release cached model references, for long-running or memory-bound hosts.""" + _MODELS.clear() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + def score(name: str, text: str) -> dict[str, float]: """Run one model on one paragraph; return {label: probability}. The single generic inference path, reused by every adapter and the downstream pipeline. Loads via the cache so repeated calls are cheap. """ + if not isinstance(text, str) or not text.strip(): + raise ValueError("text must be a non-empty string") model, tokenizer = _get(name) inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) @@ -96,14 +82,6 @@ def score(name: str, text: str) -> dict[str, float]: # Fuzzy input variable -> (model short-name, label string to read). # Single source of truth for the signal-mapping decisions. Change a label # in one place if a model ever changes its id2label. -SIGNAL_MAP = { - "specificity": ("specificity", "spec"), - "commitment": ("commitment", "yes"), - "sentiment_asymmetry": ("sentiment", "opportunity"), - "netzero": ("netzero", "net-zero"), -} - - def signal(var: str, text: str) -> float: """Return the [0, 1] signal for one fuzzy input variable.""" if var not in SIGNAL_MAP: @@ -129,6 +107,15 @@ def score_batch(name: str, texts: list[str], batch_size: int = 32) -> list[dict[ paragraphs at once. `padding=True` makes a batch's sequences equal length; the attention mask ensures padded tokens don't affect the result. """ + if name not in MODEL_REGISTRY: + raise KeyError(f"Unknown model: {name}. Known: {list(MODEL_REGISTRY)}") + if isinstance(batch_size, bool) or not isinstance(batch_size, int) or batch_size <= 0: + raise ValueError("batch_size must be a positive integer") + if not isinstance(texts, list) or any(not isinstance(text, str) or not text.strip() for text in texts): + raise ValueError("texts must be a list of non-empty strings") + if not texts: + return [] + model, tokenizer = _get(name) id2label = model.config.id2label results: list[dict[str, float]] = [] @@ -153,4 +140,4 @@ def all_signals_batch(texts: list[str], batch_size: int = 32) -> dict[str, list[ for var, (name, label) in SIGNAL_MAP.items(): rows = score_batch(name, texts, batch_size=batch_size) out[var] = [r[label] for r in rows] - return out \ No newline at end of file + return out diff --git a/src/greenrisk/provenance.py b/src/greenrisk/provenance.py new file mode 100644 index 0000000..8790d22 --- /dev/null +++ b/src/greenrisk/provenance.py @@ -0,0 +1,108 @@ +"""Per-score W3C PROV-O construction for GreenRisk outputs.""" + +from __future__ import annotations + +import hashlib +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +from prov.model import Namespace, ProvDocument + +from . import __version__ +from .metadata import ( + INSTRUMENT_COMMIT, + INSTRUMENT_METHOD, + INSTRUMENT_RULE_COUNT, + INSTRUMENT_TAG, + MODEL_REGISTRY, + SIGNAL_MAP, +) + + +def text_sha256(text: str) -> str: + return hashlib.sha256(text.encode("utf-8")).hexdigest() + + +def build_score_provenance(payload: dict[str, Any]) -> ProvDocument: + """Build a PROV-O graph from a completed ``score-text`` payload.""" + if payload.get("mode") != "text" or payload.get("status") != "scored": + raise ValueError("PROV-O output requires a completed text score") + + digest = payload["input"]["sha256"] + now = datetime.now(UTC) + doc = ProvDocument() + gr = Namespace("gr", "https://github.com/idcesares/GreenRisk/prov/") + hf = Namespace("hf", "https://huggingface.co/") + doc.set_default_namespace("https://github.com/idcesares/GreenRisk/prov/") + doc.add_namespace(gr) + doc.add_namespace(hf) + + pipeline = doc.agent( + f"gr:GreenRiskPipeline_v{__version__}", + {"prov:type": "prov:SoftwareAgent", "gr:version": __version__}, + ) + paragraph = doc.entity( + f"gr:paragraph_{digest}", + { + "prov:type": "gr:InputParagraph", + "gr:text_sha256": digest, + "gr:char_count": payload["input"]["char_count"], + }, + ) + instrument = doc.entity( + f"gr:instrument_{INSTRUMENT_TAG}", + { + "prov:type": "gr:FuzzyInstrument", + "gr:tag": INSTRUMENT_TAG, + "gr:commit": INSTRUMENT_COMMIT, + "gr:n_rules": INSTRUMENT_RULE_COUNT, + "gr:method": INSTRUMENT_METHOD, + }, + ) + activity = doc.activity( + f"gr:score_{digest}", + now, + now, + { + "prov:type": "gr:GreenRiskScoring", + "gr:gate": payload["gate"]["threshold"], + "gr:climate_probability": payload["gate"]["probability"], + }, + ) + risk = doc.entity( + f"gr:risk_{digest}", + {"prov:type": "gr:RiskScore", "gr:value": payload["risk"]}, + ) + doc.wasAssociatedWith(activity, pipeline) + doc.used(activity, paragraph) + doc.used(activity, instrument) + + for short_name in ["detector", *[SIGNAL_MAP[key][0] for key in SIGNAL_MAP]]: + model = MODEL_REGISTRY[short_name] + entity = doc.entity( + f"hf:{model['repo']}", + { + "prov:type": "gr:PretrainedModel", + "gr:short_name": short_name, + "gr:commit_hash": model["revision"], + }, + ) + doc.used(activity, entity) + + doc.wasGeneratedBy(risk, activity) + doc.wasDerivedFrom(risk, paragraph) + doc.wasDerivedFrom(risk, instrument) + return doc + + +def write_score_provenance(payload: dict[str, Any], path: Path) -> None: + """Serialize a score graph as Turtle or PROV-JSON based on the suffix.""" + document = build_score_provenance(payload) + path.parent.mkdir(parents=True, exist_ok=True) + if path.suffix.lower() == ".ttl": + document.serialize(str(path), format="rdf", rdf_format="ttl") + elif path.suffix.lower() == ".json": + document.serialize(str(path), format="json") + else: + raise ValueError("provenance path must end in .ttl or .json") diff --git a/rule_base.py b/src/greenrisk/rule_base.py similarity index 76% rename from rule_base.py rename to src/greenrisk/rule_base.py index ab3c6c6..07bdb2f 100644 --- a/rule_base.py +++ b/src/greenrisk/rule_base.py @@ -11,17 +11,19 @@ the four construct-aligned signals. """ import functools +import math import operator +from collections.abc import Mapping import skfuzzy as fuzz from skfuzzy import control as ctrl -from linguistic_variables import ( - specificity, +from .linguistic_variables import ( commitment, - sentiment_asymmetry, netzero, risk, + sentiment_asymmetry, + specificity, ) ANTS = { @@ -74,12 +76,49 @@ def _term_membership(variable, term, x): a = ANTS[variable] return float(fuzz.interp_membership(a.universe, a[term].mf, x)) -def score_paragraph(signals): +def _validated_signals(signals: Mapping[str, float]) -> dict[str, float]: + """Validate and normalize a complete GreenRisk signal mapping. + + scikit-fuzzy clips out-of-universe inputs by default. Silent clipping is + unsuitable for an auditable scoring API, so values outside [0, 1], missing + signals, unexpected signals, booleans, and non-finite numbers are rejected + before they reach the control system. + """ + if not isinstance(signals, Mapping): + raise TypeError("signals must be a mapping of signal names to probabilities") + + expected = set(ANTS) + received = set(signals) + missing = sorted(expected - received) + unexpected = sorted(received - expected) + if missing or unexpected: + details = [] + if missing: + details.append(f"missing={missing}") + if unexpected: + details.append(f"unexpected={unexpected}") + raise ValueError("invalid signal keys: " + ", ".join(details)) + + validated: dict[str, float] = {} + for name in ANTS: + value = signals[name] + if isinstance(value, bool) or not isinstance(value, (int, float)): + raise TypeError(f"{name} must be a finite number in [0, 1]") + value = float(value) + if not math.isfinite(value) or not 0.0 <= value <= 1.0: + raise ValueError(f"{name} must be a finite number in [0, 1], got {value!r}") + validated[name] = value + return validated + + +def score_paragraph(signals: Mapping[str, float]): """signals: {input_name: value in [0, 1]} for all four inputs. Returns (risk_score, trace), where trace is a list of fired rules sorted by firing strength, each {'rule', 'fire', 'consequent', 'note'}. """ + signals = _validated_signals(signals) + # Fresh simulation per call avoids scikit-fuzzy's cross-run state caching. sim = ctrl.ControlSystemSimulation(_SYSTEM) for name, value in signals.items(): @@ -90,12 +129,12 @@ def score_paragraph(signals): for rid, terms, consequent, note in RULES: firing = min(_term_membership(v, t, signals[v]) for v, t in terms) # AND = min if firing > 1e-6: - trace.append({"rule": rid, "fire": round(firing, 3), + trace.append({"rule": rid, "fire": firing, "consequent": consequent, "note": note}) trace.sort(key=lambda d: d["fire"], reverse=True) return round(float(sim.output["risk"]), 2), trace -def library_firings(signals): +def library_firings(signals: Mapping[str, float]): """Read-only audit hook: each rule's firing strength as computed by scikit-fuzzy's OWN engine (not our re-derivation). @@ -106,6 +145,7 @@ def library_firings(signals): Returns {rule_id: firing in [0, 1]} for every rule (0.0 if it did not fire). """ + signals = _validated_signals(signals) sim = ctrl.ControlSystemSimulation(_SYSTEM) for name, value in signals.items(): sim.input[name] = value @@ -119,4 +159,4 @@ def format_trace(signals, risk_score, trace): for t in trace: lines.append(f" {t['rule']:>3} fire={t['fire']:.3f} -> " f"{t['consequent']:<8} ({t['note']})") - return "\n".join(lines) \ No newline at end of file + return "\n".join(lines) diff --git a/tests/property_test_rule_base.py b/tests/property_test_rule_base.py index 9a65a61..10fafec 100644 --- a/tests/property_test_rule_base.py +++ b/tests/property_test_rule_base.py @@ -27,7 +27,7 @@ sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) -from rule_base import RULES, ANTS, RISK, score_paragraph, library_firings # noqa: E402 +from greenrisk.rule_base import ANTS, RISK, RULES, library_firings, score_paragraph # noqa: E402 TOL = 1e-6 # Small movement from centroid aggregation where two input terms overlap. @@ -75,7 +75,7 @@ def check_integrity(): spine = {tuple(t[1] for t in terms) for rid, terms, _, _ in RULES if rid.startswith("S")} expected = {(s, c) for s in ("Low", "Medium", "High") for c in ("Low", "Medium", "High")} assert spine == expected, f"spine does not tile the 3x3 plane: {expected - spine}" - print(f" integrity: 17 rules, unique ids, spine tiles 3x3 OK") + print(" integrity: 17 rules, unique ids, spine tiles 3x3 OK") def check_term_order(): @@ -160,5 +160,14 @@ def main(): print(f"All properties hold ({len(_cache)} grid points evaluated).") +def test_locked_rule_base_properties(): + """Expose the existing exhaustive checks to pytest and local scripts.""" + check_integrity() + check_term_order() + check_spine_surface() + check_amplifier_bound() + check_trace_matches_library() + + if __name__ == "__main__": main() diff --git a/tests/smoke_test_all_models.py b/tests/smoke_test_all_models.py index 5b524d6..74e780c 100644 --- a/tests/smoke_test_all_models.py +++ b/tests/smoke_test_all_models.py @@ -1,73 +1,26 @@ -# Smoke test script to verify that all models can be loaded and run on a sample paragraph. -# It also prints the label mappings and probabilities for each model +"""Optional smoke tests for all pinned model adapters.""" -import pathlib -import sys - -# Repo root on the path so core modules import when run from anywhere. -sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) - -import torch -from models import load - -# A paragraph designed to trigger interesting signals across all dimensions: -# - clearly climate-related (detector → yes) -# - somewhat vague on specifics (specificity → could go either way) -# - has commitment language (commitment → yes) -# - opportunity-framing (sentiment → opportunity) -# - contains a net-zero pledge (netzero → yes) +import pytest TEST_PARAGRAPH = ( "We are committed to achieving net-zero greenhouse gas emissions across " "our entire value chain by 2050. Our sustainability strategy places " "climate action at the core of our business, creating long-term value " - "for shareholders while contributing to a more sustainable future. We " - "continue to explore innovative solutions to reduce our environmental " - "footprint and seize the opportunities presented by the transition to " - "a low-carbon economy." + "for shareholders while contributing to a more sustainable future." ) -MODEL_NAMES = ["detector", "specificity", "commitment", "sentiment", "netzero"] - - -def run_single(model_name: str, paragraph: str) -> dict: - """Load a model, run inference on one paragraph, return structured results.""" - model, tokenizer = load(model_name) - model.eval() - inputs = tokenizer(paragraph, return_tensors="pt", truncation=True, max_length=512) +@pytest.mark.model +def test_all_pinned_models_produce_probabilities() -> None: + from greenrisk.models import MODEL_REGISTRY, score - with torch.no_grad(): - outputs = model(**inputs) - - probs = torch.nn.functional.softmax(outputs.logits, dim=-1) - id2label = model.config.id2label - - # Build a label→probability mapping - label_probs = {} - for idx, prob in enumerate(probs[0].tolist()): - label = id2label.get(idx, f"class_{idx}") - label_probs[label] = round(prob, 4) - - return { - "model": model_name, - "id2label": id2label, - "probabilities": label_probs, - } + for name in MODEL_REGISTRY: + result = score(name, TEST_PARAGRAPH) + assert result + assert all(0.0 <= probability <= 1.0 for probability in result.values()) + assert sum(result.values()) == pytest.approx(1.0, abs=1e-6) if __name__ == "__main__": - print("=" * 70) - print("GreenRisk Multi-Model Smoke Test") - print("=" * 70) - print(f"\nTest paragraph ({len(TEST_PARAGRAPH.split())} words):") - print(f" '{TEST_PARAGRAPH[:120]}...'\n") - - results = {} - for name in MODEL_NAMES: - print(f"Loading {name}...") - result = run_single(name, TEST_PARAGRAPH) - results[name] = result - print(f" Labels: {result['id2label']}") - print(f" Probabilities: {result['probabilities']}") - print() \ No newline at end of file + test_all_pinned_models_produce_probabilities() + print("All pinned model smoke tests passed.") diff --git a/tests/smoke_test_detector_model.py b/tests/smoke_test_detector_model.py index 89ae9e0..f44acc8 100644 --- a/tests/smoke_test_detector_model.py +++ b/tests/smoke_test_detector_model.py @@ -1,31 +1,24 @@ -""" This is a Sanity check: load the climate detector and run it on one paragraph. -Expected outcome: detector says 'yes' with high confidence.""" +"""Optional sanity check for the pinned climate-relevance detector.""" -import pathlib -import sys +import pytest -# Repo root on the path so core modules import when run from anywhere. -sys.path.insert(0, str(pathlib.Path(__file__).resolve().parents[1])) - -import torch -from models import load - -model, tokenizer = load("detector") -model.eval() - -paragraph = ( +PARAGRAPH = ( "We are committed to reducing our Scope 1 and Scope 2 greenhouse gas " "emissions by 50% by 2030, relative to a 2019 baseline. This commitment " "is aligned with the Science Based Targets initiative and verified annually " "by an independent third party." ) -inputs = tokenizer(paragraph, return_tensors="pt", truncation=True, max_length=512) -with torch.no_grad(): - outputs = model(**inputs) +@pytest.mark.model +def test_climate_detector_recognizes_climate_paragraph() -> None: + from greenrisk.models import score + + result = score("detector", PARAGRAPH) + assert set(result) == {"no", "yes"} + assert result["yes"] > result["no"] -probs = torch.nn.functional.softmax(outputs.logits, dim=-1) -print("Label mapping:", model.config.id2label) -print("Probabilities:", probs[0].tolist()) \ No newline at end of file +if __name__ == "__main__": + test_climate_detector_recognizes_climate_paragraph() + print("Detector smoke test passed.") diff --git a/tests/smoke_test_provenance.py b/tests/smoke_test_provenance.py index 063633b..8480740 100644 --- a/tests/smoke_test_provenance.py +++ b/tests/smoke_test_provenance.py @@ -43,11 +43,15 @@ def main() -> None: missing = [term for term in required_terms if term not in ttl_text] if missing: raise AssertionError(f"Missing provenance terms: {missing}") - if "GreenRiskPipeline_v0.1" not in json_text: + if "GreenRiskPipeline_v0.2.0" not in json_text: raise AssertionError("Missing pipeline agent in PROV-JSON output") print("Provenance smoke test passed.") +def test_provenance_roundtrip() -> None: + main() + + if __name__ == "__main__": main() diff --git a/tests/test_artifact_integrity.py b/tests/test_artifact_integrity.py new file mode 100644 index 0000000..26f02e9 --- /dev/null +++ b/tests/test_artifact_integrity.py @@ -0,0 +1,11 @@ +"""Committed research outputs remain bound to their provenance records.""" + +from scripts.verify_artifact_integrity import verify_run + + +def test_corpus_artifacts() -> None: + verify_run("corpus_run", "corpus_run") + + +def test_contrast_artifacts() -> None: + verify_run("contrast_run", "contrast_run") diff --git a/tests/test_cli.py b/tests/test_cli.py new file mode 100644 index 0000000..9fcbb40 --- /dev/null +++ b/tests/test_cli.py @@ -0,0 +1,49 @@ +"""Tests for the dependency-light signal-scoring interface.""" + +import argparse + +import pytest + +from greenrisk.cli import _score_signals, probability +from greenrisk.rule_base import score_paragraph + + +def test_signal_cli_payload() -> None: + args = argparse.Namespace( + specificity=0.2, + commitment=0.9, + sentiment_asymmetry=0.4, + netzero=0.7, + ) + payload = _score_signals(args) + assert payload["status"] == "scored" + assert payload["instrument"]["tag"] == "rulebase-locked-v1" + assert payload["risk"] == 88.33 + assert payload["trace"][0]["rule"] in {"S3", "N1"} + + +@pytest.mark.parametrize("value", ["-0.01", "1.01", "nan", "inf"]) +def test_cli_rejects_invalid_probability(value: str) -> None: + with pytest.raises((argparse.ArgumentTypeError, ValueError)): + probability(value) + + +@pytest.mark.parametrize( + "signals", + [ + { + "specificity": 1.5, + "commitment": 0.5, + "sentiment_asymmetry": 0.5, + "netzero": 0.5, + }, + { + "specificity": 0.5, + "commitment": 0.5, + "sentiment_asymmetry": 0.5, + }, + ], +) +def test_scoring_rejects_invalid_signal_contract(signals: dict[str, float]) -> None: + with pytest.raises(ValueError): + score_paragraph(signals) diff --git a/tests/test_instrument_lock.py b/tests/test_instrument_lock.py new file mode 100644 index 0000000..1ff7f92 --- /dev/null +++ b/tests/test_instrument_lock.py @@ -0,0 +1,60 @@ +"""Machine-readable guardrails for coordinates fixed by rulebase-locked-v1.""" + +import hashlib +import json + +from greenrisk.linguistic_variables import INPUTS, risk +from greenrisk.metadata import ( + INSTRUMENT_COMMIT, + MODEL_REGISTRY, + SIGNAL_MAP, + TCFD_DATASET_REVISION, +) +from greenrisk.rule_base import RULES + + +def test_locked_coordinates() -> None: + assert INSTRUMENT_COMMIT == "a40288ac8eacfe0de1987884ec03bea9457972c5" + assert TCFD_DATASET_REVISION == "aee5c98f0bf7835bfb08308ffc7c17216657976b" + assert SIGNAL_MAP == { + "specificity": ("specificity", "spec"), + "commitment": ("commitment", "yes"), + "sentiment_asymmetry": ("sentiment", "opportunity"), + "netzero": ("netzero", "net-zero"), + } + assert {name: value["revision"] for name, value in MODEL_REGISTRY.items()} == { + "detector": "2c3bc660d45a59e31b35f5d3e365ee4f59fdf76c", + "specificity": "4ada96ed4bf5c3a7a711282e41f1ab9b29f0ddea", + "commitment": "17337c3292df16a8fe93b1505dfe4122d50a4c91", + "sentiment": "e9f9a94ee4263f5ad5cfc97b8539a497fc88aa7d", + "netzero": "25cf57e30613a2156fee1fe3f917036df4a5c0d1", + } + assert len(RULES) == 17 + + +def test_locked_numerical_payload_hash() -> None: + """Hash all numerical/model coordinates while excluding explanatory notes.""" + payload = { + "models": MODEL_REGISTRY, + "signals": {name: list(mapping) for name, mapping in SIGNAL_MAP.items()}, + "rules": [ + [rule_id, [list(term) for term in terms], consequent] + for rule_id, terms, consequent, _note in RULES + ], + "inputs": { + variable.label: { + term: variable[term].mf.tolist() for term in ("Low", "Medium", "High") + } + for variable in INPUTS + }, + "risk": { + term: risk[term].mf.tolist() + for term in ("Low", "Moderate", "Elevated", "High") + }, + } + canonical = json.dumps( + payload, sort_keys=True, separators=(",", ":"), allow_nan=False + ).encode() + assert hashlib.sha256(canonical).hexdigest() == ( + "841fc8fb5ee2dbe4d3cfd55fa3154cc22cc50907575d878aa3b40a1bdfe2190b" + ) diff --git a/uv.lock b/uv.lock index ac7303e..8739dee 100644 --- a/uv.lock +++ b/uv.lock @@ -272,6 +272,20 @@ css = [ { name = "tinycss2" }, ] +[[package]] +name = "build" +version = "1.6.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "os_name == 'nt' and sys_platform != 'linux'" }, + { name = "packaging" }, + { name = "pyproject-hooks" }, +] +sdist = { url = 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files changed, 7 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d22e72b..db872d6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,9 +14,10 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5 - - uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 with: enable-cache: true + version: "0.11.33" - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6 with: python-version: "3.12" diff --git a/.github/workflows/model-smoke.yml b/.github/workflows/model-smoke.yml index 5d5825e..30a3a0b 100644 --- a/.github/workflows/model-smoke.yml +++ b/.github/workflows/model-smoke.yml @@ -12,9 +12,10 @@ jobs: timeout-minutes: 45 steps: - uses: actions/checkout@fbc6f3992d24b796d5a048ff273f7fcc4a7b6c09 # v5 - - uses: astral-sh/setup-uv@d0cc045d04ccac9d8b7881df0226f9e82c39688e # v6 + - uses: astral-sh/setup-uv@37802adc94f370d6bfd71619e3f0bf239e1f3b78 # v7 with: enable-cache: true + version: "0.11.33" - uses: actions/setup-python@ece7cb06caefa5fff74198d8649806c4678c61a1 # v6 with: python-version: "3.12" diff --git a/pyproject.toml b/pyproject.toml index 993fbf9..e0f6d88 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,6 +9,7 @@ readme = "README.md" license = "Apache-2.0" requires-python = ">=3.12" dependencies = [ + "lxml>=5.0.0", "networkx>=3.6.1", "numpy>=2.3.0", "prov>=2.1.1", @@ -27,7 +28,6 @@ research = [ "datasets>=4.8.5", "graphviz>=0.21", "jupyterlab>=4.5.7", - "lxml>=5.0.0", "matplotlib>=3.11.0", "networkx>=3.6.1", "pandas>=3.0.0", diff --git a/uv.lock b/uv.lock index 8739dee..c8f883d 100644 --- a/uv.lock +++ b/uv.lock @@ -866,6 +866,7 @@ name = "greenrisk" version = "0.2.0" source = { editable = "." } dependencies = [ + { name = "lxml" }, { name = "networkx" }, { name = "numpy" }, { name = "prov" }, @@ -884,7 +885,6 @@ research = [ { name = "datasets" }, { name = "graphviz" }, { name = "jupyterlab" }, - { name = "lxml" }, { name = "matplotlib" }, { name = "networkx" }, { name = "pandas" }, @@ -909,7 +909,7 @@ requires-dist = [ { name = "graphviz", marker = "extra == 'research'", specifier = ">=0.21" }, { name = "huggingface-hub", marker = "extra == 'models'", specifier = ">=1.13.0" }, { name = "jupyterlab", marker = "extra == 'research'", specifier = ">=4.5.7" }, - { name = "lxml", marker = "extra == 'research'", specifier = ">=5.0.0" }, + { name = "lxml", specifier = ">=5.0.0" }, { name = "matplotlib", marker = "extra == 'research'", specifier = ">=3.11.0" }, { name = "networkx", specifier = ">=3.6.1" }, { name = "networkx", marker = "extra == 'research'", specifier = ">=3.6.1" },