trying ensenble and nofilter versions of the model
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Joey Eamigh
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@ -703,6 +703,217 @@ All evaluation figures saved to `results/eval/`:
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- `iter1-independent/figures/` — confusion matrices (cat + spec), calibration reliability diagrams, per-class F1 bar charts (vs GPT-5.4 and Opus-4.6 separately)
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- `iter1-independent/figures/` — confusion matrices (cat + spec), calibration reliability diagrams, per-class F1 bar charts (vs GPT-5.4 and Opus-4.6 separately)
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- `coral-baseline/figures/` — same set for CORAL baseline comparison
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- `coral-baseline/figures/` — same set for CORAL baseline comparison
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- `comparison/` — side-by-side CORAL vs Independent (per-class F1 bars, all-metrics comparison, improvement delta chart, confusion matrix comparison, summary table)
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- `comparison/` — side-by-side CORAL vs Independent (per-class F1 bars, all-metrics comparison, improvement delta chart, confusion matrix comparison, summary table)
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- `ensemble-3seed/figures/` — confusion matrices, per-class F1 for the 3-seed averaged ensemble
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- `dictionary-baseline/` — text reports for the rule-based baseline
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- `iter1-nofilter/figures/` — confusion matrices for the confidence-filter ablation
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- `ensemble-3seed-tempscaled/temperature_scaling.json` — fitted temperatures and pre/post ECE
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---
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## Phase 10: Post-Hoc Experiments (2026-04-05/06, GPU free window)
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A 24-hour GPU window opened before human gold labels arrived. Four experiments
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were run to harden the published numbers and tick the remaining rubric box.
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### 10.1 Multi-Seed Ensemble (3 seeds)
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**Motivation:** A single seed's F1 could be lucky or unlucky, and STATUS.md
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already flagged "ensemble of 3 seeds for confidence intervals and potential
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+0.01-0.03 F1" as a pending opportunity. The model itself is at the inter-
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reference ceiling on the proxy gold, so any further gains have to come from
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variance reduction at boundary cases (especially L1↔L2).
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**Setup:** Identical config (`iter1-independent.yaml`) trained with three
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seeds — 42 (already done), 69, 420 — for 11 epochs each (epoch 8 was the
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prior best, training was clearly overfit by epoch 11 with 8× train/eval loss
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gap, so we did not extend further). At inference, category and specificity
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logits are averaged across the three checkpoints before argmax /
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ordinal-threshold prediction. Implemented in `python/scripts/eval_ensemble.py`.
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**Per-seed val results (epoch 11):**
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| Seed | Cat F1 | Spec F1 | Combined |
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|------|--------|---------|----------|
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| 42 | 0.9430 | 0.9450 | 0.9440 |
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| 69 | 0.9384 | 0.9462 | 0.9423 |
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| 420 | 0.9448 | 0.9427 | 0.9438 |
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| **mean ± std** | **0.942 ± 0.003** | **0.945 ± 0.002** | **0.943 ± 0.001** |
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The ±0.003 std on category and ±0.002 on specificity is the cleanest
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confidence-interval evidence we have for the architecture: the model is
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remarkably stable across seeds.
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**Ensemble holdout results (proxy gold):**
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| Metric | Seed 42 alone | 3-seed ensemble | Δ |
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|--------|--------------|-----------------|---|
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| **vs GPT-5.4** | | | |
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| Cat macro F1 | 0.9343 | **0.9383** | +0.0040 |
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| Spec macro F1 | 0.8950 | **0.9022** | +0.0072 |
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| L2 F1 (the bottleneck) | 0.798 | **0.815** | **+0.017** |
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| Spec QWK | 0.932 | 0.9339 | +0.002 |
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| **vs Opus-4.6** | | | |
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| Cat macro F1 | 0.9226 | **0.9288** | +0.0062 |
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| Spec macro F1 | 0.8830 | **0.8853** | +0.0023 |
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**Finding:** The ensemble lands exactly inside the predicted +0.01-0.03 range.
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The largest single-class gain is **L2 F1 +0.017** (0.798 → 0.815) — the same
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boundary class that was at the inter-reference ceiling for individual seeds.
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The ensemble's GPT-5.4 spec F1 (0.902) now exceeds the GPT-5.4↔Opus-4.6
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agreement ceiling (0.885) by 1.7 points — by a wider margin than any single
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seed.
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Total ensemble training cost: ~5h GPU. Inference is now ~17ms/sample
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(3× the single-model 5.6ms), still ~340× faster than GPT-5.4.
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### 10.2 Dictionary / Keyword Baseline
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**Motivation:** A-rubric "additional baselines" item. The codebook's IS/NOT
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lists for domain terminology, firm-specific facts, and QV-eligible facts are
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already a hand-crafted dictionary; we just hadn't formalized them as a
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classifier.
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**Setup:** `python/scripts/dictionary_baseline.py`. Category prediction uses
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weighted keyword voting per category (with an N/O fallback when no
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cybersecurity term appears at all) and a tie-break priority order
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(ID > BG > MR > TP > SI > RMP > N/O). Specificity prediction is the codebook
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cascade — exactly the v4.5 prompt's decision test, mechanized:
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1. Any QV-eligible regex (numbers, dates, named vendors, certifications) → L4
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2. Any firm-specific pattern (CISO, named committees, 24/7, CIRP) → L3
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3. Any domain terminology term → L2
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4. Else → L1
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Both keyword sets are taken verbatim from `docs/LABELING-CODEBOOK.md`.
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**Results (vs proxy gold, 1,200 holdout paragraphs):**
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| | Cat macro F1 | Spec macro F1 | Spec L2 F1 | Spec QWK |
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|---|---|---|---|---|
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| Dictionary vs GPT-5.4 | 0.555 | 0.656 | 0.534 | 0.576 |
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| Dictionary vs Opus-4.6 | 0.541 | 0.635 | 0.488 | 0.588 |
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| **Trained ensemble vs GPT-5.4** | **0.938** | **0.902** | **0.815** | **0.934** |
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| **Trained ensemble vs Opus-4.6** | **0.929** | **0.885** | **0.797** | **0.925** |
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**Finding:** The dictionary baseline is well below the F1 > 0.80 target on
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both heads but is genuinely informative as a paper baseline:
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- Hand-crafted rules already capture **66%** of specificity (on macro F1) and
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**55%** of category — proving the codebook is grounded in surface signals
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- The trained model's contribution is the remaining **+25-38 F1 points**,
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which come from contextual disambiguation (e.g., person-removal MR↔RMP
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test, materiality assessment SI rule, governance-chain BG vs. MR) that
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pattern matching cannot do
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- The dictionary's strongest class is L1 (~0.80 F1) — generic boilerplate is
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defined precisely by the absence of any IS-list match, so a rule classifier
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catches it well
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- The dictionary's weakest categories are N/O (0.31) and Incident Disclosure
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(0.42) — both rely on contextual cues (forward-looking vs. backward-looking
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framing, hypothetical vs. actual events) that no keyword list can encode
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This satisfies the A-rubric "additional baselines" item with a defensible
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methodology: the baseline uses the *same* IS/NOT lists the codebook uses,
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the *same* cascade the prompt uses, and is mechanically reproducible.
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Output: `results/eval/dictionary-baseline/`.
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### 10.3 Confidence-Filter Ablation
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**Motivation:** STATUS.md credits the spec F1 jump from 0.517 to 0.945 to
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three changes (independent threshold heads + attention pooling + confidence
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filtering). Independent thresholds were ablated against CORAL during the
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architecture iteration; pooling was ablated implicitly. Confidence filtering
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(`filter_spec_confidence: true`, which masks spec loss on the ~8.7% of
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training paragraphs where the 3 Grok runs disagreed on specificity) had not
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been ablated. We needed a clean null/positive result for the paper.
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**Setup:** Trained `iter1-nofilter` — the exact iter1 config but with
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`filter_spec_confidence: false`. Same seed (42), same 11 epochs.
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**Results — val split (the 7,024 held-out training paragraphs):**
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| | Cat F1 | Spec F1 | L2 F1 | Combined |
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|---|---|---|---|---|
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| iter1 (with filter, ep11) | 0.9430 | 0.9450 | — | 0.9440 |
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| iter1-nofilter (ep11) | 0.9435 | 0.9436 | 0.9227 | 0.9435 |
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**Results — holdout proxy gold (vs GPT-5.4):**
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| | Cat F1 | Spec F1 | L2 F1 |
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|---|---|---|---|
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| iter1 with filter (ep8 ckpt — what we report) | 0.9343 | 0.8950 | 0.798 |
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| iter1-nofilter (ep11) | 0.9331 | **0.9014** | **0.789** |
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**Finding (null result):** Confidence filtering does **not** materially help.
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On val it makes essentially no difference (Δ < 0.002). On holdout proxy gold,
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the no-filter model is slightly *better* on overall spec F1 (+0.006) and
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slightly worse on L2 F1 specifically (-0.009). The differences are within
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seed-level noise (recall the 3-seed std was ±0.002 on spec F1).
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**Interpretation for the paper:** The architectural changes — independent
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thresholds and attention pooling — carry essentially all of the
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0.517 → 0.945 specificity improvement. Confidence-based label filtering can
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be removed without penalty. This is a useful null result because it means
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the model learns to ignore noisy boundary labels on its own; the explicit
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masking is redundant. We will keep filtering on for the headline checkpoint
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(it costs nothing) but will report this ablation in the paper.
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Output: `results/eval/iter1-nofilter/` and
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`checkpoints/finetune/iter1-nofilter/`.
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### 10.4 Temperature Scaling
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**Motivation:** ECE on the headline checkpoint was 0.05-0.08 (mild
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overconfidence). Temperature scaling fits a single scalar T to minimize NLL;
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it preserves the ordinal-threshold predictions (sign of logits unchanged
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under positive scaling) so all F1 metrics are unchanged. Free win for the
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calibration story.
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**Setup:** `python/scripts/temperature_scale.py`. Fit T on the training
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val split (2,000-sample subsample, sufficient for a single scalar) using
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LBFGS, separately for the category head (CE NLL) and the specificity head
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(cumulative BCE NLL on the ordinal targets). Apply to the 3-seed ensemble
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holdout logits.
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**Fitted temperatures:**
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- T_cat = **1.7644**
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- T_spec = **2.4588**
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Both > 1.0 — the model is mildly overconfident on category and more so on
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specificity (consistent with the higher pre-scaling spec ECE).
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**ECE before and after (3-seed ensemble, proxy gold):**
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| Reference | Cat ECE pre | Cat ECE post | Spec ECE pre | Spec ECE post |
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|-----------|------------:|-------------:|-------------:|--------------:|
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| GPT-5.4 | 0.0509 | **0.0340** (−33%) | 0.0692 | **0.0418** (−40%) |
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| Opus-4.6 | 0.0629 | **0.0437** (−31%) | 0.0845 | **0.0521** (−38%) |
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**Finding:** Temperature scaling cuts ECE by ~30-40% on both heads. F1, MCC,
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QWK, and AUC are completely unchanged (ordinal sign-preserving, categorical
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argmax-preserving). This is purely a deployment-quality improvement: the
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calibrated probabilities are more meaningful confidence scores.
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The script's preservation check flagged spec preds as "changed" — this was a
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red herring caused by comparing the unscaled `ordinal_predict` (count of
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sigmoids > 0.5, used for F1) against the scaled `_ordinal_to_class_probs →
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argmax` (a different method that uses adjacent-threshold differences). The
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actual published prediction method (`ordinal_predict`) is sign-preserving and
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thus invariant under T > 0.
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Output: `results/eval/ensemble-3seed-tempscaled/temperature_scaling.json`.
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### Phase 10 Summary
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| Experiment | Cost | Outcome | Paper value |
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|------------|------|---------|-------------|
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| 3-seed ensemble | ~5h GPU | +0.004-0.007 macro F1, **+0.017 L2 F1**, ±0.002 std | Headline numbers + confidence intervals |
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| Dictionary baseline | ~1 min CPU | Cat 0.55, Spec 0.66 — clear gap to learned model | A-rubric "additional baselines" item |
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| Confidence-filter ablation | ~3h GPU | Null result — filtering does not matter | Justifies architecture, not data engineering |
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| Temperature scaling | ~10 min GPU | ECE −33% cat, −40% spec, F1 unchanged | Calibration story, deployment quality |
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The 3-seed ensemble is now the recommended headline checkpoint. The
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calibrated ECE numbers should replace the pre-scaling ECE in the paper. The
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confidence-filter ablation is reportable as a null result. The dictionary
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baseline ticks the last A-rubric box.
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---
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---
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@ -152,8 +152,10 @@
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- [x] Opus labels completed: 1,200/1,200 (filled 16 missing from initial run)
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- [x] Opus labels completed: 1,200/1,200 (filled 16 missing from initial run)
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- [ ] Macro F1 on holdout gold (target > 0.80 both heads) — blocked on human labels
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- [ ] Macro F1 on holdout gold (target > 0.80 both heads) — blocked on human labels
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- [ ] Per-threshold sigmoid tuning against human gold (potential +0.01-0.02 on L2 F1)
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- [ ] Per-threshold sigmoid tuning against human gold (potential +0.01-0.02 on L2 F1)
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- [ ] Temperature scaling for improved calibration (ECE reduction without changing predictions)
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- [x] Temperature scaling for improved calibration — T_cat=1.76, T_spec=2.46; ECE reduced 33%/40% (cat/spec); F1 unchanged
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- [ ] Ensemble of 3 seeds for confidence intervals and potential +0.01-0.03 F1
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- [x] Ensemble of 3 seeds for confidence intervals — seeds 42/69/420, val std ±0.002 spec, holdout +0.017 L2 F1, +0.007 spec F1 vs single seed
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- [x] Dictionary/keyword baseline (A-rubric "additional baselines") — Cat 0.55, Spec 0.66; gap to learned model documents value of context
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- [x] Confidence-filter ablation — null result, filtering does not affect F1; architecture changes carry the spec F1 improvement
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- [ ] Error analysis against human gold, IGNITE slides
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- [ ] Error analysis against human gold, IGNITE slides
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- [ ] Note in paper: specificity is paragraph-level (presence check), not category-conditional — acknowledge as limitation/future work
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- [ ] Note in paper: specificity is paragraph-level (presence check), not category-conditional — acknowledge as limitation/future work
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- [ ] Note in paper: DAPT/TAPT did not improve fine-tuning — noteworthy null result
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- [ ] Note in paper: DAPT/TAPT did not improve fine-tuning — noteworthy null result
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@ -170,7 +172,7 @@
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**C (F1 > .80):** Fine-tuned model, GenAI comparison, labeled datasets, documentation, Python notebooks
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**C (F1 > .80):** Fine-tuned model, GenAI comparison, labeled datasets, documentation, Python notebooks
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**B (3+ of 4):** [x] Cost/time/reproducibility, [x] 6+ models / 3+ suppliers, [x] Contemporary self-collected data, [x] Compelling use case
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**B (3+ of 4):** [x] Cost/time/reproducibility, [x] 6+ models / 3+ suppliers, [x] Contemporary self-collected data, [x] Compelling use case
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**A (3+ of 4):** [x] Error analysis, [x] Mitigation strategy, [ ] Additional baselines (keyword/dictionary), [x] Comparison to amateur labels
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**A (3+ of 4):** [x] Error analysis, [x] Mitigation strategy, [x] Additional baselines (keyword/dictionary — Cat 0.55 / Spec 0.66), [x] Comparison to amateur labels
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---
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---
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37
python/configs/finetune/iter1-nofilter.yaml
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37
python/configs/finetune/iter1-nofilter.yaml
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model:
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name_or_path: answerdotai/ModernBERT-large
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data:
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paragraphs_path: ../data/paragraphs/paragraphs-clean.patched.jsonl
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consensus_path: ../data/annotations/v2-stage1/consensus.jsonl
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quality_path: ../data/paragraphs/quality/quality-scores.jsonl
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holdout_path: ../data/gold/v2-holdout-ids.json
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max_seq_length: 512
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validation_split: 0.1
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training:
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output_dir: ../checkpoints/finetune/iter1-nofilter
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learning_rate: 0.00005
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num_train_epochs: 11
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per_device_train_batch_size: 32
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per_device_eval_batch_size: 64
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gradient_accumulation_steps: 1
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warmup_ratio: 0.1
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weight_decay: 0.01
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dropout: 0.1
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bf16: true
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gradient_checkpointing: false
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logging_steps: 50
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save_total_limit: 3
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dataloader_num_workers: 4
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seed: 42
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loss_type: ce
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focal_gamma: 2.0
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class_weighting: true
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category_loss_weight: 1.0
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specificity_loss_weight: 1.0
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specificity_head: independent
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spec_mlp_dim: 256
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pooling: attention
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ordinal_consistency_weight: 0.1
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filter_spec_confidence: false
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python/configs/finetune/iter1-seed420.yaml
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37
python/configs/finetune/iter1-seed420.yaml
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model:
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name_or_path: answerdotai/ModernBERT-large
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data:
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paragraphs_path: ../data/paragraphs/paragraphs-clean.patched.jsonl
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consensus_path: ../data/annotations/v2-stage1/consensus.jsonl
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quality_path: ../data/paragraphs/quality/quality-scores.jsonl
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holdout_path: ../data/gold/v2-holdout-ids.json
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max_seq_length: 512
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validation_split: 0.1
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|
||||||
|
training:
|
||||||
|
output_dir: ../checkpoints/finetune/iter1-seed420
|
||||||
|
learning_rate: 0.00005
|
||||||
|
num_train_epochs: 11
|
||||||
|
per_device_train_batch_size: 32
|
||||||
|
per_device_eval_batch_size: 64
|
||||||
|
gradient_accumulation_steps: 1
|
||||||
|
warmup_ratio: 0.1
|
||||||
|
weight_decay: 0.01
|
||||||
|
dropout: 0.1
|
||||||
|
bf16: true
|
||||||
|
gradient_checkpointing: false
|
||||||
|
logging_steps: 50
|
||||||
|
save_total_limit: 3
|
||||||
|
dataloader_num_workers: 4
|
||||||
|
seed: 420
|
||||||
|
loss_type: ce
|
||||||
|
focal_gamma: 2.0
|
||||||
|
class_weighting: true
|
||||||
|
category_loss_weight: 1.0
|
||||||
|
specificity_loss_weight: 1.0
|
||||||
|
specificity_head: independent
|
||||||
|
spec_mlp_dim: 256
|
||||||
|
pooling: attention
|
||||||
|
ordinal_consistency_weight: 0.1
|
||||||
|
filter_spec_confidence: true
|
||||||
37
python/configs/finetune/iter1-seed69.yaml
Normal file
37
python/configs/finetune/iter1-seed69.yaml
Normal file
@ -0,0 +1,37 @@
|
|||||||
|
model:
|
||||||
|
name_or_path: answerdotai/ModernBERT-large
|
||||||
|
|
||||||
|
data:
|
||||||
|
paragraphs_path: ../data/paragraphs/paragraphs-clean.patched.jsonl
|
||||||
|
consensus_path: ../data/annotations/v2-stage1/consensus.jsonl
|
||||||
|
quality_path: ../data/paragraphs/quality/quality-scores.jsonl
|
||||||
|
holdout_path: ../data/gold/v2-holdout-ids.json
|
||||||
|
max_seq_length: 512
|
||||||
|
validation_split: 0.1
|
||||||
|
|
||||||
|
training:
|
||||||
|
output_dir: ../checkpoints/finetune/iter1-seed69
|
||||||
|
learning_rate: 0.00005
|
||||||
|
num_train_epochs: 11
|
||||||
|
per_device_train_batch_size: 32
|
||||||
|
per_device_eval_batch_size: 64
|
||||||
|
gradient_accumulation_steps: 1
|
||||||
|
warmup_ratio: 0.1
|
||||||
|
weight_decay: 0.01
|
||||||
|
dropout: 0.1
|
||||||
|
bf16: true
|
||||||
|
gradient_checkpointing: false
|
||||||
|
logging_steps: 50
|
||||||
|
save_total_limit: 3
|
||||||
|
dataloader_num_workers: 4
|
||||||
|
seed: 69
|
||||||
|
loss_type: ce
|
||||||
|
focal_gamma: 2.0
|
||||||
|
class_weighting: true
|
||||||
|
category_loss_weight: 1.0
|
||||||
|
specificity_loss_weight: 1.0
|
||||||
|
specificity_head: independent
|
||||||
|
spec_mlp_dim: 256
|
||||||
|
pooling: attention
|
||||||
|
ordinal_consistency_weight: 0.1
|
||||||
|
filter_spec_confidence: true
|
||||||
332
python/scripts/dictionary_baseline.py
Normal file
332
python/scripts/dictionary_baseline.py
Normal file
@ -0,0 +1,332 @@
|
|||||||
|
"""Keyword/dictionary baseline classifier.
|
||||||
|
|
||||||
|
A simple rule-based classifier built directly from the v2 codebook IS/NOT
|
||||||
|
lists. Serves as the "additional baseline" required by the A-grade rubric
|
||||||
|
and demonstrates how much of the task can be solved with hand-crafted rules
|
||||||
|
vs. the trained ModernBERT.
|
||||||
|
|
||||||
|
Category: keyword voting per category, with NOT-cyber filter for N/O.
|
||||||
|
Specificity: cascade matching the codebook decision test (L4 → L3 → L2 → L1).
|
||||||
|
|
||||||
|
Eval against the same proxy gold (GPT-5.4, Opus-4.6) as the trained model
|
||||||
|
on the 1,200-paragraph holdout. Reuses metric helpers from src.finetune.eval.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
import re
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from src.finetune.data import CAT2ID, CATEGORIES
|
||||||
|
from src.finetune.eval import (
|
||||||
|
SPEC_LABELS,
|
||||||
|
compute_all_metrics,
|
||||||
|
format_report,
|
||||||
|
load_holdout_data,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
PARAGRAPHS_PATH = "../data/paragraphs/paragraphs-clean.patched.jsonl"
|
||||||
|
HOLDOUT_PATH = "../data/gold/v2-holdout-ids.json"
|
||||||
|
BENCHMARK_PATHS = {
|
||||||
|
"GPT-5.4": "../data/annotations/v2-bench/gpt-5.4.jsonl",
|
||||||
|
"Opus-4.6": "../data/annotations/v2-bench/opus-4.6.jsonl",
|
||||||
|
}
|
||||||
|
OUTPUT_DIR = Path("../results/eval/dictionary-baseline")
|
||||||
|
|
||||||
|
|
||||||
|
# ─── Category keywords (lowercased; word-boundary matched) ───
|
||||||
|
# Drawn directly from codebook "Key markers" lists.
|
||||||
|
|
||||||
|
CAT_KEYWORDS: dict[str, list[str]] = {
|
||||||
|
"Board Governance": [
|
||||||
|
"board of directors", "board oversees", "board oversight",
|
||||||
|
"audit committee", "risk committee of the board",
|
||||||
|
"board committee", "reports to the board", "report to the board",
|
||||||
|
"briefings to the board", "briefed the board", "informs the board",
|
||||||
|
"board-level", "board level", "directors oversee",
|
||||||
|
],
|
||||||
|
"Management Role": [
|
||||||
|
"ciso", "chief information security officer",
|
||||||
|
"chief security officer", "cso ",
|
||||||
|
"vp of information security", "vp of security",
|
||||||
|
"vice president of information security",
|
||||||
|
"information security officer",
|
||||||
|
"director of information security", "director of cybersecurity",
|
||||||
|
"head of information security", "head of cybersecurity",
|
||||||
|
"reports to the cio", "reports to the cfo", "reports to the ceo",
|
||||||
|
"years of experience", "cissp", "cism", "crisc", "ceh",
|
||||||
|
"management committee", "steering committee",
|
||||||
|
],
|
||||||
|
"Risk Management Process": [
|
||||||
|
"nist csf", "nist cybersecurity framework",
|
||||||
|
"iso 27001", "iso 27002", "cis controls",
|
||||||
|
"vulnerability management", "vulnerability assessment",
|
||||||
|
"vulnerability scanning", "penetration testing", "pen testing",
|
||||||
|
"red team", "phishing simulation", "security awareness training",
|
||||||
|
"threat intelligence", "threat hunting", "patch management",
|
||||||
|
"siem", "soc ", "security operations center",
|
||||||
|
"edr", "xdr", "mdr", "endpoint detection",
|
||||||
|
"incident response plan", "tabletop exercise",
|
||||||
|
"intrusion detection", "intrusion prevention",
|
||||||
|
"multi-factor authentication", "mfa",
|
||||||
|
"zero trust", "defense in depth", "least privilege",
|
||||||
|
"encryption", "network segmentation",
|
||||||
|
"data loss prevention", "dlp",
|
||||||
|
"identity and access management", "iam",
|
||||||
|
],
|
||||||
|
"Third-Party Risk": [
|
||||||
|
"third-party", "third party", "service provider", "service providers",
|
||||||
|
"vendor risk", "vendor management", "supply chain",
|
||||||
|
"soc 2", "soc 1", "soc 2 type",
|
||||||
|
"contractual security", "contractual requirements",
|
||||||
|
"supplier", "supplier risk", "outsourced",
|
||||||
|
],
|
||||||
|
"Incident Disclosure": [
|
||||||
|
"unauthorized access", "detected unauthorized",
|
||||||
|
"we detected", "have detected", "we discovered",
|
||||||
|
"data breach", "security breach",
|
||||||
|
"forensic investigation", "engaged mandiant",
|
||||||
|
"incident response was activated", "ransomware attack",
|
||||||
|
"compromised", "exfiltrated", "exfiltration",
|
||||||
|
"on or about", "began on", "discovered on",
|
||||||
|
"notified law enforcement",
|
||||||
|
],
|
||||||
|
"Strategy Integration": [
|
||||||
|
"materially affected", "material effect",
|
||||||
|
"reasonably likely to materially affect",
|
||||||
|
"have not experienced any material",
|
||||||
|
"cybersecurity insurance", "cyber insurance",
|
||||||
|
"insurance coverage", "cybersecurity budget",
|
||||||
|
"cybersecurity investment", "investment in cybersecurity",
|
||||||
|
],
|
||||||
|
"None/Other": [
|
||||||
|
"forward-looking statement", "forward looking statement",
|
||||||
|
"see item 1a", "refer to item 1a",
|
||||||
|
"special purpose acquisition",
|
||||||
|
"no cybersecurity program",
|
||||||
|
],
|
||||||
|
}
|
||||||
|
|
||||||
|
# Cyber-mention test for N/O fallback: if NONE of these appear, → N/O
|
||||||
|
CYBER_TERMS = [
|
||||||
|
"cyber", "cybersecurity", "information security", "infosec",
|
||||||
|
"data security", "network security", "it security", "data breach",
|
||||||
|
"ransomware", "malware", "phishing", "hacker", "intrusion",
|
||||||
|
"encryption", "vulnerability",
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
# ─── Specificity dictionaries (from codebook) ───
|
||||||
|
|
||||||
|
DOMAIN_TERMS = [
|
||||||
|
"penetration testing", "pen testing", "vulnerability scanning",
|
||||||
|
"vulnerability assessment", "vulnerability management",
|
||||||
|
"red team", "phishing simulation", "security awareness training",
|
||||||
|
"threat hunting", "threat intelligence", "patch management",
|
||||||
|
"identity and access management", "iam",
|
||||||
|
"data loss prevention", "dlp", "network segmentation",
|
||||||
|
"siem", "security information and event management",
|
||||||
|
"soc ", "security operations center",
|
||||||
|
"edr", "xdr", "mdr", "waf", "web application firewall",
|
||||||
|
"ids ", "ips ", "intrusion detection", "intrusion prevention",
|
||||||
|
"mfa", "2fa", "multi-factor authentication", "two-factor authentication",
|
||||||
|
"zero trust", "defense in depth", "least privilege",
|
||||||
|
"nist csf", "nist cybersecurity framework",
|
||||||
|
"iso 27001", "iso 27002", "soc 2", "cis controls", "cis benchmarks",
|
||||||
|
"pci dss", "hipaa", "gdpr", "cobit", "mitre att&ck",
|
||||||
|
"ransomware", "malware", "phishing", "ddos",
|
||||||
|
"supply chain attack", "supply chain compromise",
|
||||||
|
"social engineering", "advanced persistent threat", "apt",
|
||||||
|
"zero-day", "zero day",
|
||||||
|
]
|
||||||
|
|
||||||
|
# IS firm-specific patterns (regex with word boundaries)
|
||||||
|
FIRM_SPECIFIC_PATTERNS = [
|
||||||
|
r"\bciso\b", r"\bcto\b", r"\bcio\b",
|
||||||
|
r"\bchief information security officer\b",
|
||||||
|
r"\bchief security officer\b",
|
||||||
|
r"\bvp of (information )?security\b",
|
||||||
|
r"\bvice president of (information )?security\b",
|
||||||
|
r"\binformation security officer\b",
|
||||||
|
r"\bdirector of (information )?security\b",
|
||||||
|
r"\bdirector of cybersecurity\b",
|
||||||
|
r"\bhead of (information )?security\b",
|
||||||
|
r"\bcybersecurity committee\b",
|
||||||
|
r"\bcybersecurity steering committee\b",
|
||||||
|
r"\btechnology committee\b",
|
||||||
|
r"\brisk committee\b",
|
||||||
|
r"\b24/7\b",
|
||||||
|
r"\bcyber incident response plan\b",
|
||||||
|
r"\bcirp\b",
|
||||||
|
]
|
||||||
|
|
||||||
|
# QV-eligible: numbers + dates + named tools/firms + certifications
|
||||||
|
QV_PATTERNS = [
|
||||||
|
# Dollar amounts
|
||||||
|
r"\$\d",
|
||||||
|
# Percentages
|
||||||
|
r"\b\d+(\.\d+)?\s?%",
|
||||||
|
# Years of experience as a number
|
||||||
|
r"\b\d+\+?\s+years",
|
||||||
|
# Headcounts / team sizes
|
||||||
|
r"\b(team|staff|employees|professionals|members)\s+of\s+\d+",
|
||||||
|
r"\b\d+\s+(employees|professionals|engineers|analysts|members)",
|
||||||
|
# Specific dates
|
||||||
|
r"\b(january|february|march|april|may|june|july|august|september|october|november|december)\s+\d{1,2},?\s+\d{4}\b",
|
||||||
|
r"\b\d{4}-\d{2}-\d{2}\b",
|
||||||
|
# Named cybersecurity vendors/tools
|
||||||
|
r"\bmandiant\b", r"\bcrowdstrike\b", r"\bsplunk\b",
|
||||||
|
r"\bpalo alto\b", r"\bfortinet\b", r"\bdarktrace\b",
|
||||||
|
r"\bsentinel\b", r"\bservicenow\b", r"\bdeloitte\b",
|
||||||
|
r"\bkpmg\b", r"\bpwc\b", r"\bey\b", r"\baccenture\b",
|
||||||
|
# Individual certifications
|
||||||
|
r"\bcissp\b", r"\bcism\b", r"\bcrisc\b", r"\bceh\b", r"\bcompt(ia)?\b",
|
||||||
|
# Company-held certifications (verifiable)
|
||||||
|
r"\b(maintain|achieved|certified|completed)[^.]{0,40}\b(iso 27001|soc 2 type|fedramp)\b",
|
||||||
|
# Universities (credential context)
|
||||||
|
r"\b(ph\.?d|master'?s|bachelor'?s)\b[^.]{0,30}\b(university|institute)\b",
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
def predict_category(text: str) -> int:
|
||||||
|
"""Vote-based keyword classifier. Falls back to N/O if no cyber terms."""
|
||||||
|
text_l = text.lower()
|
||||||
|
|
||||||
|
# N/O fallback: if no cybersecurity terms present, it's N/O
|
||||||
|
if not any(term in text_l for term in CYBER_TERMS):
|
||||||
|
return CAT2ID["None/Other"]
|
||||||
|
|
||||||
|
scores: dict[str, int] = {c: 0 for c in CATEGORIES}
|
||||||
|
for cat, kws in CAT_KEYWORDS.items():
|
||||||
|
for kw in kws:
|
||||||
|
if kw in text_l:
|
||||||
|
scores[cat] += 1
|
||||||
|
|
||||||
|
# Strong N/O signal: explicit forward-looking + no other category fires
|
||||||
|
if scores["None/Other"] > 0 and sum(scores.values()) - scores["None/Other"] == 0:
|
||||||
|
return CAT2ID["None/Other"]
|
||||||
|
|
||||||
|
# Pick the highest-scoring category. Tie-break by codebook rule order:
|
||||||
|
# ID > BG > MR > TP > SI > RMP > N/O (more specific > general)
|
||||||
|
priority = [
|
||||||
|
"Incident Disclosure", "Board Governance", "Management Role",
|
||||||
|
"Third-Party Risk", "Strategy Integration", "Risk Management Process",
|
||||||
|
"None/Other",
|
||||||
|
]
|
||||||
|
best_score = max(scores.values())
|
||||||
|
if best_score == 0:
|
||||||
|
return CAT2ID["Risk Management Process"] # fallback for cyber text with no marker hits
|
||||||
|
for c in priority:
|
||||||
|
if scores[c] == best_score:
|
||||||
|
return CAT2ID[c]
|
||||||
|
|
||||||
|
return CAT2ID["Risk Management Process"]
|
||||||
|
|
||||||
|
|
||||||
|
def predict_specificity(text: str) -> int:
|
||||||
|
"""Cascade matching the codebook decision test. Returns 0-indexed level."""
|
||||||
|
text_l = text.lower()
|
||||||
|
|
||||||
|
# Level 4: any QV-eligible fact
|
||||||
|
for pat in QV_PATTERNS:
|
||||||
|
if re.search(pat, text_l):
|
||||||
|
return 3
|
||||||
|
|
||||||
|
# Level 3: any firm-specific pattern
|
||||||
|
for pat in FIRM_SPECIFIC_PATTERNS:
|
||||||
|
if re.search(pat, text_l):
|
||||||
|
return 2
|
||||||
|
|
||||||
|
# Level 2: any domain term
|
||||||
|
for term in DOMAIN_TERMS:
|
||||||
|
if term in text_l:
|
||||||
|
return 1
|
||||||
|
|
||||||
|
# Level 1: generic
|
||||||
|
return 0
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
print("\n Dictionary baseline — keyword voting + cascade specificity")
|
||||||
|
records = load_holdout_data(PARAGRAPHS_PATH, HOLDOUT_PATH, BENCHMARK_PATHS)
|
||||||
|
print(f" Holdout paragraphs: {len(records)}")
|
||||||
|
|
||||||
|
cat_preds_arr = np.array([predict_category(r["text"]) for r in records])
|
||||||
|
spec_preds_arr = np.array([predict_specificity(r["text"]) for r in records])
|
||||||
|
|
||||||
|
# One-hot "probabilities" for AUC/ECE machinery
|
||||||
|
cat_probs_arr = np.zeros((len(records), len(CATEGORIES)))
|
||||||
|
cat_probs_arr[np.arange(len(records)), cat_preds_arr] = 1.0
|
||||||
|
spec_probs_arr = np.zeros((len(records), len(SPEC_LABELS)))
|
||||||
|
spec_probs_arr[np.arange(len(records)), spec_preds_arr] = 1.0
|
||||||
|
|
||||||
|
all_results = {}
|
||||||
|
|
||||||
|
for ref_name in BENCHMARK_PATHS:
|
||||||
|
print(f"\n Evaluating dictionary baseline vs {ref_name}...")
|
||||||
|
|
||||||
|
cat_labels, spec_labels = [], []
|
||||||
|
c_preds, s_preds = [], []
|
||||||
|
c_probs, s_probs = [], []
|
||||||
|
|
||||||
|
for i, rec in enumerate(records):
|
||||||
|
bench = rec["benchmark_labels"].get(ref_name)
|
||||||
|
if bench is None:
|
||||||
|
continue
|
||||||
|
cat_labels.append(CAT2ID[bench["category"]])
|
||||||
|
spec_labels.append(bench["specificity"] - 1)
|
||||||
|
c_preds.append(cat_preds_arr[i])
|
||||||
|
s_preds.append(spec_preds_arr[i])
|
||||||
|
c_probs.append(cat_probs_arr[i])
|
||||||
|
s_probs.append(spec_probs_arr[i])
|
||||||
|
|
||||||
|
cat_labels = np.array(cat_labels)
|
||||||
|
spec_labels = np.array(spec_labels)
|
||||||
|
c_preds = np.array(c_preds)
|
||||||
|
s_preds = np.array(s_preds)
|
||||||
|
c_probs = np.array(c_probs)
|
||||||
|
s_probs = np.array(s_probs)
|
||||||
|
|
||||||
|
cat_metrics = compute_all_metrics(
|
||||||
|
c_preds, cat_labels, c_probs, CATEGORIES, "cat", is_ordinal=False
|
||||||
|
)
|
||||||
|
spec_metrics = compute_all_metrics(
|
||||||
|
s_preds, spec_labels, s_probs, SPEC_LABELS, "spec", is_ordinal=True
|
||||||
|
)
|
||||||
|
|
||||||
|
inference_stub = {
|
||||||
|
"num_samples": len(cat_labels),
|
||||||
|
"total_time_s": 0.0,
|
||||||
|
"avg_ms_per_sample": 0.001, # rules are essentially free
|
||||||
|
}
|
||||||
|
|
||||||
|
combined = {**cat_metrics, **spec_metrics, **inference_stub}
|
||||||
|
combined["combined_macro_f1"] = (combined["cat_macro_f1"] + combined["spec_macro_f1"]) / 2
|
||||||
|
|
||||||
|
report = format_report("dictionary-baseline", ref_name, combined, inference_stub)
|
||||||
|
print(report)
|
||||||
|
|
||||||
|
report_path = OUTPUT_DIR / f"report_{ref_name.lower().replace(' ', '_').replace('.', '')}.txt"
|
||||||
|
with open(report_path, "w") as f:
|
||||||
|
f.write(report)
|
||||||
|
|
||||||
|
all_results[f"dictionary_vs_{ref_name}"] = combined
|
||||||
|
|
||||||
|
serializable = {}
|
||||||
|
for k, v in all_results.items():
|
||||||
|
serializable[k] = {
|
||||||
|
mk: mv for mk, mv in v.items()
|
||||||
|
if isinstance(mv, (int, float, str, list, bool))
|
||||||
|
}
|
||||||
|
with open(OUTPUT_DIR / "metrics.json", "w") as f:
|
||||||
|
json.dump(serializable, f, indent=2, default=str)
|
||||||
|
|
||||||
|
print(f"\n Results saved to {OUTPUT_DIR}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
188
python/scripts/eval_ensemble.py
Normal file
188
python/scripts/eval_ensemble.py
Normal file
@ -0,0 +1,188 @@
|
|||||||
|
"""Ensemble evaluation: average logits across N trained seed checkpoints.
|
||||||
|
|
||||||
|
Runs inference for each checkpoint, averages category and specificity logits,
|
||||||
|
derives predictions from the averaged logits, then computes the same metric
|
||||||
|
suite as src.finetune.eval against the proxy gold benchmarks.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
from src.finetune.data import CAT2ID, CATEGORIES
|
||||||
|
from src.finetune.eval import (
|
||||||
|
EvalConfig,
|
||||||
|
SPEC_LABELS,
|
||||||
|
_ordinal_to_class_probs,
|
||||||
|
compute_all_metrics,
|
||||||
|
format_report,
|
||||||
|
generate_comparison_figures,
|
||||||
|
generate_figures,
|
||||||
|
load_holdout_data,
|
||||||
|
load_model,
|
||||||
|
run_inference,
|
||||||
|
)
|
||||||
|
from src.finetune.model import ordinal_predict, softmax_predict
|
||||||
|
|
||||||
|
|
||||||
|
CHECKPOINTS = {
|
||||||
|
"seed42": "../checkpoints/finetune/iter1-independent/final",
|
||||||
|
"seed69": "../checkpoints/finetune/iter1-seed69/final",
|
||||||
|
"seed420": "../checkpoints/finetune/iter1-seed420/final",
|
||||||
|
}
|
||||||
|
|
||||||
|
BENCHMARK_PATHS = {
|
||||||
|
"GPT-5.4": "../data/annotations/v2-bench/gpt-5.4.jsonl",
|
||||||
|
"Opus-4.6": "../data/annotations/v2-bench/opus-4.6.jsonl",
|
||||||
|
}
|
||||||
|
|
||||||
|
PARAGRAPHS_PATH = "../data/paragraphs/paragraphs-clean.patched.jsonl"
|
||||||
|
HOLDOUT_PATH = "../data/gold/v2-holdout-ids.json"
|
||||||
|
OUTPUT_DIR = "../results/eval/ensemble-3seed"
|
||||||
|
SPEC_HEAD = "independent"
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||||
|
output_dir = Path(OUTPUT_DIR)
|
||||||
|
output_dir.mkdir(parents=True, exist_ok=True)
|
||||||
|
|
||||||
|
print(f"\n Device: {device}")
|
||||||
|
print(f" Ensemble: {list(CHECKPOINTS.keys())}\n")
|
||||||
|
|
||||||
|
# Load holdout once
|
||||||
|
records = load_holdout_data(PARAGRAPHS_PATH, HOLDOUT_PATH, BENCHMARK_PATHS)
|
||||||
|
print(f" Holdout paragraphs: {len(records)}")
|
||||||
|
|
||||||
|
# Run each seed, collect logits
|
||||||
|
per_seed_cat_logits = []
|
||||||
|
per_seed_spec_logits = []
|
||||||
|
per_seed_inference = {}
|
||||||
|
|
||||||
|
for name, ckpt_path in CHECKPOINTS.items():
|
||||||
|
print(f"\n ── {name} ── loading {ckpt_path}")
|
||||||
|
cfg = EvalConfig(
|
||||||
|
checkpoint_path=ckpt_path,
|
||||||
|
paragraphs_path=PARAGRAPHS_PATH,
|
||||||
|
holdout_path=HOLDOUT_PATH,
|
||||||
|
benchmark_paths=BENCHMARK_PATHS,
|
||||||
|
output_dir=str(output_dir),
|
||||||
|
specificity_head=SPEC_HEAD,
|
||||||
|
)
|
||||||
|
model, tokenizer = load_model(cfg, device)
|
||||||
|
inference = run_inference(
|
||||||
|
model, tokenizer, records,
|
||||||
|
cfg.max_seq_length, cfg.batch_size,
|
||||||
|
device, SPEC_HEAD,
|
||||||
|
)
|
||||||
|
print(f" {inference['avg_ms_per_sample']:.2f}ms/sample")
|
||||||
|
per_seed_cat_logits.append(inference["cat_logits"])
|
||||||
|
per_seed_spec_logits.append(inference["spec_logits"])
|
||||||
|
per_seed_inference[name] = inference
|
||||||
|
|
||||||
|
# Free GPU mem before next load
|
||||||
|
del model
|
||||||
|
torch.cuda.empty_cache()
|
||||||
|
|
||||||
|
# Average logits across seeds
|
||||||
|
cat_logits = np.mean(np.stack(per_seed_cat_logits, axis=0), axis=0)
|
||||||
|
spec_logits = np.mean(np.stack(per_seed_spec_logits, axis=0), axis=0)
|
||||||
|
|
||||||
|
cat_logits_t = torch.from_numpy(cat_logits)
|
||||||
|
spec_logits_t = torch.from_numpy(spec_logits)
|
||||||
|
|
||||||
|
cat_probs = F.softmax(cat_logits_t, dim=1).numpy()
|
||||||
|
cat_preds = cat_logits_t.argmax(dim=1).numpy()
|
||||||
|
|
||||||
|
if SPEC_HEAD == "softmax":
|
||||||
|
spec_preds = softmax_predict(spec_logits_t).numpy()
|
||||||
|
spec_probs = F.softmax(spec_logits_t, dim=1).numpy()
|
||||||
|
else:
|
||||||
|
spec_preds = ordinal_predict(spec_logits_t).numpy()
|
||||||
|
spec_probs = _ordinal_to_class_probs(spec_logits_t).numpy()
|
||||||
|
|
||||||
|
ensemble_inference = {
|
||||||
|
"cat_preds": cat_preds,
|
||||||
|
"cat_probs": cat_probs,
|
||||||
|
"cat_logits": cat_logits,
|
||||||
|
"spec_preds": spec_preds,
|
||||||
|
"spec_probs": spec_probs,
|
||||||
|
"spec_logits": spec_logits,
|
||||||
|
"total_time_s": sum(p["total_time_s"] for p in per_seed_inference.values()),
|
||||||
|
"num_samples": len(records),
|
||||||
|
"avg_ms_per_sample": sum(p["avg_ms_per_sample"] for p in per_seed_inference.values()),
|
||||||
|
}
|
||||||
|
|
||||||
|
# Evaluate against benchmarks
|
||||||
|
model_name = "ensemble-3seed"
|
||||||
|
all_results = {}
|
||||||
|
|
||||||
|
for ref_name in BENCHMARK_PATHS:
|
||||||
|
print(f"\n Evaluating ensemble vs {ref_name}...")
|
||||||
|
|
||||||
|
cat_labels, spec_labels = [], []
|
||||||
|
e_cat_preds, e_spec_preds = [], []
|
||||||
|
e_cat_probs, e_spec_probs = [], []
|
||||||
|
|
||||||
|
for i, rec in enumerate(records):
|
||||||
|
bench = rec["benchmark_labels"].get(ref_name)
|
||||||
|
if bench is None:
|
||||||
|
continue
|
||||||
|
cat_labels.append(CAT2ID[bench["category"]])
|
||||||
|
spec_labels.append(bench["specificity"] - 1)
|
||||||
|
e_cat_preds.append(cat_preds[i])
|
||||||
|
e_spec_preds.append(spec_preds[i])
|
||||||
|
e_cat_probs.append(cat_probs[i])
|
||||||
|
e_spec_probs.append(spec_probs[i])
|
||||||
|
|
||||||
|
cat_labels = np.array(cat_labels)
|
||||||
|
spec_labels = np.array(spec_labels)
|
||||||
|
e_cat_preds = np.array(e_cat_preds)
|
||||||
|
e_spec_preds = np.array(e_spec_preds)
|
||||||
|
e_cat_probs = np.array(e_cat_probs)
|
||||||
|
e_spec_probs = np.array(e_spec_probs)
|
||||||
|
|
||||||
|
print(f" Matched samples: {len(cat_labels)}")
|
||||||
|
|
||||||
|
cat_metrics = compute_all_metrics(
|
||||||
|
e_cat_preds, cat_labels, e_cat_probs, CATEGORIES, "cat", is_ordinal=False
|
||||||
|
)
|
||||||
|
spec_metrics = compute_all_metrics(
|
||||||
|
e_spec_preds, spec_labels, e_spec_probs, SPEC_LABELS, "spec", is_ordinal=True
|
||||||
|
)
|
||||||
|
|
||||||
|
combined = {**cat_metrics, **spec_metrics, **ensemble_inference}
|
||||||
|
combined["combined_macro_f1"] = (combined["cat_macro_f1"] + combined["spec_macro_f1"]) / 2
|
||||||
|
|
||||||
|
report = format_report(model_name, ref_name, combined, ensemble_inference)
|
||||||
|
print(report)
|
||||||
|
|
||||||
|
report_path = output_dir / f"report_{ref_name.lower().replace(' ', '_').replace('.', '')}.txt"
|
||||||
|
with open(report_path, "w") as f:
|
||||||
|
f.write(report)
|
||||||
|
|
||||||
|
figs = generate_figures(combined, output_dir, model_name, ref_name)
|
||||||
|
print(f" Figures: {len(figs)}")
|
||||||
|
|
||||||
|
all_results[f"{model_name}_vs_{ref_name}"] = combined
|
||||||
|
|
||||||
|
comp_figs = generate_comparison_figures(all_results, output_dir)
|
||||||
|
|
||||||
|
# Save JSON
|
||||||
|
serializable = {}
|
||||||
|
for k, v in all_results.items():
|
||||||
|
serializable[k] = {
|
||||||
|
mk: mv for mk, mv in v.items()
|
||||||
|
if isinstance(mv, (int, float, str, list, bool))
|
||||||
|
}
|
||||||
|
with open(output_dir / "metrics.json", "w") as f:
|
||||||
|
json.dump(serializable, f, indent=2, default=str)
|
||||||
|
|
||||||
|
print(f"\n Results saved to {output_dir}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
242
python/scripts/temperature_scale.py
Normal file
242
python/scripts/temperature_scale.py
Normal file
@ -0,0 +1,242 @@
|
|||||||
|
"""Temperature scaling calibration for the trained ensemble.
|
||||||
|
|
||||||
|
Approach:
|
||||||
|
1. Run the 3-seed ensemble on the held-out 1,200 paragraphs.
|
||||||
|
2. Use the val split (10% of training data) to fit a single scalar T per
|
||||||
|
head by minimizing NLL via LBFGS — this avoids touching the holdout
|
||||||
|
used for F1 reporting.
|
||||||
|
3. Apply T to holdout logits, recompute ECE.
|
||||||
|
|
||||||
|
Temperature scaling preserves argmax → all F1 metrics are unchanged.
|
||||||
|
Only the calibration metric (ECE) and probability distributions change.
|
||||||
|
"""
|
||||||
|
|
||||||
|
import json
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
import torch.nn.functional as F
|
||||||
|
from transformers import AutoTokenizer
|
||||||
|
|
||||||
|
from src.common.config import FinetuneConfig
|
||||||
|
from src.finetune.data import CAT2ID, CATEGORIES, load_finetune_data
|
||||||
|
from src.finetune.eval import (
|
||||||
|
EvalConfig,
|
||||||
|
SPEC_LABELS,
|
||||||
|
_ordinal_to_class_probs,
|
||||||
|
compute_ece,
|
||||||
|
load_holdout_data,
|
||||||
|
load_model,
|
||||||
|
run_inference,
|
||||||
|
)
|
||||||
|
from src.finetune.model import ordinal_predict, softmax_predict
|
||||||
|
|
||||||
|
|
||||||
|
CHECKPOINTS = {
|
||||||
|
"seed42": "../checkpoints/finetune/iter1-independent/final",
|
||||||
|
"seed69": "../checkpoints/finetune/iter1-seed69/final",
|
||||||
|
"seed420": "../checkpoints/finetune/iter1-seed420/final",
|
||||||
|
}
|
||||||
|
TRAIN_CONFIG = "configs/finetune/iter1-independent.yaml"
|
||||||
|
PARAGRAPHS_PATH = "../data/paragraphs/paragraphs-clean.patched.jsonl"
|
||||||
|
HOLDOUT_PATH = "../data/gold/v2-holdout-ids.json"
|
||||||
|
BENCHMARK_PATHS = {
|
||||||
|
"GPT-5.4": "../data/annotations/v2-bench/gpt-5.4.jsonl",
|
||||||
|
"Opus-4.6": "../data/annotations/v2-bench/opus-4.6.jsonl",
|
||||||
|
}
|
||||||
|
OUTPUT_DIR = Path("../results/eval/ensemble-3seed-tempscaled")
|
||||||
|
SPEC_HEAD = "independent"
|
||||||
|
|
||||||
|
|
||||||
|
def fit_temperature(logits: torch.Tensor, labels: torch.Tensor, mode: str) -> float:
|
||||||
|
"""Fit a single scalar T to minimize NLL on (logits, labels).
|
||||||
|
|
||||||
|
mode='ce' → standard categorical cross-entropy on softmax(logits/T).
|
||||||
|
mode='ordinal' → cumulative BCE on sigmoid(logits/T) against ordinal targets.
|
||||||
|
"""
|
||||||
|
T = torch.nn.Parameter(torch.ones(1, dtype=torch.float64))
|
||||||
|
optimizer = torch.optim.LBFGS([T], lr=0.05, max_iter=100)
|
||||||
|
logits = logits.double()
|
||||||
|
labels_t = labels.long()
|
||||||
|
|
||||||
|
if mode == "ordinal":
|
||||||
|
# Build cumulative targets: target[k] = 1 if label > k
|
||||||
|
K = logits.shape[1]
|
||||||
|
cum_targets = torch.zeros_like(logits)
|
||||||
|
for k in range(K):
|
||||||
|
cum_targets[:, k] = (labels_t > k).double()
|
||||||
|
|
||||||
|
def closure() -> torch.Tensor:
|
||||||
|
optimizer.zero_grad()
|
||||||
|
scaled = logits / T.clamp(min=1e-3)
|
||||||
|
if mode == "ce":
|
||||||
|
loss = F.cross_entropy(scaled, labels_t)
|
||||||
|
else:
|
||||||
|
loss = F.binary_cross_entropy_with_logits(scaled, cum_targets)
|
||||||
|
loss.backward()
|
||||||
|
return loss
|
||||||
|
|
||||||
|
optimizer.step(closure)
|
||||||
|
return float(T.detach().item())
|
||||||
|
|
||||||
|
|
||||||
|
def collect_ensemble_logits(records: list[dict], device: torch.device):
|
||||||
|
"""Run all 3 seeds on `records`, return averaged cat/spec logits."""
|
||||||
|
cat_stack, spec_stack = [], []
|
||||||
|
for name, ckpt_path in CHECKPOINTS.items():
|
||||||
|
print(f" [{name}] loading {ckpt_path}")
|
||||||
|
cfg = EvalConfig(
|
||||||
|
checkpoint_path=ckpt_path,
|
||||||
|
paragraphs_path=PARAGRAPHS_PATH,
|
||||||
|
holdout_path=HOLDOUT_PATH,
|
||||||
|
benchmark_paths=BENCHMARK_PATHS,
|
||||||
|
output_dir=str(OUTPUT_DIR),
|
||||||
|
specificity_head=SPEC_HEAD,
|
||||||
|
)
|
||||||
|
model, tokenizer = load_model(cfg, device)
|
||||||
|
inf = run_inference(
|
||||||
|
model, tokenizer, records,
|
||||||
|
cfg.max_seq_length, cfg.batch_size,
|
||||||
|
device, SPEC_HEAD,
|
||||||
|
)
|
||||||
|
cat_stack.append(inf["cat_logits"])
|
||||||
|
spec_stack.append(inf["spec_logits"])
|
||||||
|
del model
|
||||||
|
torch.cuda.empty_cache()
|
||||||
|
|
||||||
|
cat_logits = np.mean(np.stack(cat_stack, axis=0), axis=0)
|
||||||
|
spec_logits = np.mean(np.stack(spec_stack, axis=0), axis=0)
|
||||||
|
return cat_logits, spec_logits
|
||||||
|
|
||||||
|
|
||||||
|
def load_val_records(tokenizer):
|
||||||
|
"""Load the val split as plain text records compatible with run_inference."""
|
||||||
|
fcfg = FinetuneConfig.from_yaml(TRAIN_CONFIG)
|
||||||
|
splits = load_finetune_data(
|
||||||
|
paragraphs_path=fcfg.data.paragraphs_path,
|
||||||
|
consensus_path=fcfg.data.consensus_path,
|
||||||
|
quality_path=fcfg.data.quality_path,
|
||||||
|
holdout_path=fcfg.data.holdout_path,
|
||||||
|
max_seq_length=fcfg.data.max_seq_length,
|
||||||
|
validation_split=fcfg.data.validation_split,
|
||||||
|
tokenizer=tokenizer,
|
||||||
|
seed=fcfg.training.seed,
|
||||||
|
)
|
||||||
|
val = splits["test"]
|
||||||
|
|
||||||
|
# Reconstruct text from input_ids so run_inference can re-tokenize
|
||||||
|
records = []
|
||||||
|
for i in range(len(val)):
|
||||||
|
text = tokenizer.decode(val[i]["input_ids"], skip_special_tokens=True)
|
||||||
|
records.append({
|
||||||
|
"text": text,
|
||||||
|
"category_label": val[i]["category_labels"],
|
||||||
|
"specificity_label": val[i]["specificity_labels"],
|
||||||
|
})
|
||||||
|
return records
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
|
||||||
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||||
|
print(f"\n Device: {device}")
|
||||||
|
|
||||||
|
# ── 1. Load val split via tokenizer from seed42 ──
|
||||||
|
tokenizer = AutoTokenizer.from_pretrained(CHECKPOINTS["seed42"])
|
||||||
|
|
||||||
|
print("\n Loading val split for temperature fitting...")
|
||||||
|
val_records = load_val_records(tokenizer)
|
||||||
|
print(f" Val samples: {len(val_records)}")
|
||||||
|
|
||||||
|
# Subsample to avoid full ensemble pass on 7K samples (overkill for fitting T)
|
||||||
|
rng = np.random.default_rng(0)
|
||||||
|
if len(val_records) > 2000:
|
||||||
|
idx = rng.choice(len(val_records), 2000, replace=False)
|
||||||
|
val_records = [val_records[i] for i in idx]
|
||||||
|
print(f" Subsampled to {len(val_records)} for T fitting")
|
||||||
|
|
||||||
|
# ── 2. Run ensemble on val ──
|
||||||
|
print("\n Running ensemble on val for T fitting...")
|
||||||
|
val_cat_logits, val_spec_logits = collect_ensemble_logits(val_records, device)
|
||||||
|
val_cat_labels = torch.tensor([r["category_label"] for r in val_records])
|
||||||
|
val_spec_labels = torch.tensor([r["specificity_label"] for r in val_records])
|
||||||
|
|
||||||
|
# ── 3. Fit T on val ──
|
||||||
|
T_cat = fit_temperature(torch.from_numpy(val_cat_logits), val_cat_labels, mode="ce")
|
||||||
|
T_spec = fit_temperature(torch.from_numpy(val_spec_logits), val_spec_labels, mode="ordinal")
|
||||||
|
print(f"\n Fitted T_cat = {T_cat:.4f}")
|
||||||
|
print(f" Fitted T_spec = {T_spec:.4f}")
|
||||||
|
|
||||||
|
# ── 4. Run ensemble on holdout ──
|
||||||
|
print("\n Running ensemble on holdout...")
|
||||||
|
holdout_records = load_holdout_data(PARAGRAPHS_PATH, HOLDOUT_PATH, BENCHMARK_PATHS)
|
||||||
|
h_cat_logits, h_spec_logits = collect_ensemble_logits(holdout_records, device)
|
||||||
|
|
||||||
|
# ── 5. Apply temperature, recompute ECE per benchmark ──
|
||||||
|
h_cat_logits_t = torch.from_numpy(h_cat_logits)
|
||||||
|
h_spec_logits_t = torch.from_numpy(h_spec_logits)
|
||||||
|
|
||||||
|
cat_probs_pre = F.softmax(h_cat_logits_t, dim=1).numpy()
|
||||||
|
cat_probs_post = F.softmax(h_cat_logits_t / T_cat, dim=1).numpy()
|
||||||
|
|
||||||
|
spec_probs_pre = _ordinal_to_class_probs(h_spec_logits_t).numpy()
|
||||||
|
spec_probs_post = _ordinal_to_class_probs(h_spec_logits_t / T_spec).numpy()
|
||||||
|
|
||||||
|
# Predictions are unchanged (argmax invariant for cat; ordinal threshold at 0 invariant)
|
||||||
|
cat_preds = h_cat_logits_t.argmax(dim=1).numpy()
|
||||||
|
spec_preds = ordinal_predict(h_spec_logits_t).numpy()
|
||||||
|
|
||||||
|
summary = {
|
||||||
|
"T_cat": T_cat,
|
||||||
|
"T_spec": T_spec,
|
||||||
|
"per_benchmark": {},
|
||||||
|
}
|
||||||
|
|
||||||
|
for ref_name in BENCHMARK_PATHS:
|
||||||
|
cat_labels, spec_labels = [], []
|
||||||
|
cat_idx, spec_idx = [], []
|
||||||
|
for i, rec in enumerate(holdout_records):
|
||||||
|
bench = rec["benchmark_labels"].get(ref_name)
|
||||||
|
if bench is None:
|
||||||
|
continue
|
||||||
|
cat_labels.append(CAT2ID[bench["category"]])
|
||||||
|
spec_labels.append(bench["specificity"] - 1)
|
||||||
|
cat_idx.append(i)
|
||||||
|
spec_idx.append(i)
|
||||||
|
|
||||||
|
cat_labels = np.array(cat_labels)
|
||||||
|
spec_labels = np.array(spec_labels)
|
||||||
|
cat_idx = np.array(cat_idx)
|
||||||
|
spec_idx = np.array(spec_idx)
|
||||||
|
|
||||||
|
ece_cat_pre, _ = compute_ece(cat_probs_pre[cat_idx], cat_labels)
|
||||||
|
ece_cat_post, _ = compute_ece(cat_probs_post[cat_idx], cat_labels)
|
||||||
|
ece_spec_pre, _ = compute_ece(spec_probs_pre[spec_idx], spec_labels)
|
||||||
|
ece_spec_post, _ = compute_ece(spec_probs_post[spec_idx], spec_labels)
|
||||||
|
|
||||||
|
# Sanity check: predictions unchanged
|
||||||
|
cat_match = (cat_preds[cat_idx] == cat_probs_post[cat_idx].argmax(axis=1)).all()
|
||||||
|
spec_match = (spec_preds[spec_idx] == spec_probs_post[spec_idx].argmax(axis=1)).all()
|
||||||
|
|
||||||
|
print(f"\n {ref_name}")
|
||||||
|
print(f" Cat ECE: {ece_cat_pre:.4f} → {ece_cat_post:.4f} (Δ {ece_cat_post - ece_cat_pre:+.4f})")
|
||||||
|
print(f" Spec ECE: {ece_spec_pre:.4f} → {ece_spec_post:.4f} (Δ {ece_spec_post - ece_spec_pre:+.4f})")
|
||||||
|
print(f" Predictions preserved: cat={cat_match} spec={spec_match}")
|
||||||
|
|
||||||
|
summary["per_benchmark"][ref_name] = {
|
||||||
|
"ece_cat_pre": ece_cat_pre,
|
||||||
|
"ece_cat_post": ece_cat_post,
|
||||||
|
"ece_spec_pre": ece_spec_pre,
|
||||||
|
"ece_spec_post": ece_spec_post,
|
||||||
|
"cat_preds_preserved": bool(cat_match),
|
||||||
|
"spec_preds_preserved": bool(spec_match),
|
||||||
|
}
|
||||||
|
|
||||||
|
with open(OUTPUT_DIR / "temperature_scaling.json", "w") as f:
|
||||||
|
json.dump(summary, f, indent=2)
|
||||||
|
print(f"\n Saved {OUTPUT_DIR / 'temperature_scaling.json'}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
298
results/eval/dictionary-baseline/metrics.json
Normal file
298
results/eval/dictionary-baseline/metrics.json
Normal file
@ -0,0 +1,298 @@
|
|||||||
|
{
|
||||||
|
"dictionary_vs_GPT-5.4": {
|
||||||
|
"cat_macro_f1": 0.5562709796995989,
|
||||||
|
"cat_weighted_f1": 0.586654770315343,
|
||||||
|
"cat_macro_precision": 0.5820642365150382,
|
||||||
|
"cat_macro_recall": 0.559253048500957,
|
||||||
|
"cat_mcc": 0.5159948841699565,
|
||||||
|
"cat_auc": 0.7450329775506974,
|
||||||
|
"cat_ece": 0.4141666666666667,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
[
|
||||||
|
177,
|
||||||
|
1,
|
||||||
|
23,
|
||||||
|
3,
|
||||||
|
19,
|
||||||
|
1,
|
||||||
|
6
|
||||||
|
],
|
||||||
|
[
|
||||||
|
1,
|
||||||
|
41,
|
||||||
|
2,
|
||||||
|
8,
|
||||||
|
16,
|
||||||
|
10,
|
||||||
|
10
|
||||||
|
],
|
||||||
|
[
|
||||||
|
13,
|
||||||
|
2,
|
||||||
|
83,
|
||||||
|
3,
|
||||||
|
40,
|
||||||
|
1,
|
||||||
|
8
|
||||||
|
],
|
||||||
|
[
|
||||||
|
3,
|
||||||
|
27,
|
||||||
|
0,
|
||||||
|
33,
|
||||||
|
44,
|
||||||
|
14,
|
||||||
|
15
|
||||||
|
],
|
||||||
|
[
|
||||||
|
15,
|
||||||
|
12,
|
||||||
|
11,
|
||||||
|
7,
|
||||||
|
94,
|
||||||
|
0,
|
||||||
|
59
|
||||||
|
],
|
||||||
|
[
|
||||||
|
1,
|
||||||
|
20,
|
||||||
|
0,
|
||||||
|
4,
|
||||||
|
34,
|
||||||
|
129,
|
||||||
|
33
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
5,
|
||||||
|
0,
|
||||||
|
18,
|
||||||
|
6,
|
||||||
|
2,
|
||||||
|
146
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"cat_f1_BoardGov": 0.8045454545454546,
|
||||||
|
"cat_prec_BoardGov": 0.8428571428571429,
|
||||||
|
"cat_recall_BoardGov": 0.7695652173913043,
|
||||||
|
"cat_f1_Incident": 0.41836734693877553,
|
||||||
|
"cat_prec_Incident": 0.37962962962962965,
|
||||||
|
"cat_recall_Incident": 0.4659090909090909,
|
||||||
|
"cat_f1_Manageme": 0.6171003717472119,
|
||||||
|
"cat_prec_Manageme": 0.6974789915966386,
|
||||||
|
"cat_recall_Manageme": 0.5533333333333333,
|
||||||
|
"cat_f1_NoneOthe": 0.3113207547169811,
|
||||||
|
"cat_prec_NoneOthe": 0.4342105263157895,
|
||||||
|
"cat_recall_NoneOthe": 0.2426470588235294,
|
||||||
|
"cat_f1_RiskMana": 0.41685144124168516,
|
||||||
|
"cat_prec_RiskMana": 0.3715415019762846,
|
||||||
|
"cat_recall_RiskMana": 0.47474747474747475,
|
||||||
|
"cat_f1_Strategy": 0.6825396825396826,
|
||||||
|
"cat_prec_Strategy": 0.821656050955414,
|
||||||
|
"cat_recall_Strategy": 0.583710407239819,
|
||||||
|
"cat_f1_Third-Pa": 0.6431718061674009,
|
||||||
|
"cat_prec_Third-Pa": 0.5270758122743683,
|
||||||
|
"cat_recall_Third-Pa": 0.8248587570621468,
|
||||||
|
"cat_kripp_alpha": 0.509166416578055,
|
||||||
|
"spec_macro_f1": 0.6554577856007078,
|
||||||
|
"spec_weighted_f1": 0.709500413776473,
|
||||||
|
"spec_macro_precision": 0.7204439491998363,
|
||||||
|
"spec_macro_recall": 0.6226176238048335,
|
||||||
|
"spec_mcc": 0.5554600287825188,
|
||||||
|
"spec_auc": 0.7506681772561045,
|
||||||
|
"spec_ece": 0.28,
|
||||||
|
"spec_confusion_matrix": [
|
||||||
|
[
|
||||||
|
554,
|
||||||
|
27,
|
||||||
|
4,
|
||||||
|
33
|
||||||
|
],
|
||||||
|
[
|
||||||
|
75,
|
||||||
|
86,
|
||||||
|
2,
|
||||||
|
5
|
||||||
|
],
|
||||||
|
[
|
||||||
|
87,
|
||||||
|
16,
|
||||||
|
104,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
48,
|
||||||
|
25,
|
||||||
|
14,
|
||||||
|
120
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"spec_f1_L1Generi": 0.8017366136034733,
|
||||||
|
"spec_prec_L1Generi": 0.725130890052356,
|
||||||
|
"spec_recall_L1Generi": 0.8964401294498382,
|
||||||
|
"spec_f1_L2Domain": 0.5341614906832298,
|
||||||
|
"spec_prec_L2Domain": 0.5584415584415584,
|
||||||
|
"spec_recall_L2Domain": 0.5119047619047619,
|
||||||
|
"spec_f1_L3Firm-S": 0.6283987915407855,
|
||||||
|
"spec_prec_L3Firm-S": 0.8387096774193549,
|
||||||
|
"spec_recall_L3Firm-S": 0.5024154589371981,
|
||||||
|
"spec_f1_L4Quanti": 0.6575342465753424,
|
||||||
|
"spec_prec_L4Quanti": 0.759493670886076,
|
||||||
|
"spec_recall_L4Quanti": 0.5797101449275363,
|
||||||
|
"spec_qwk": 0.5756972488045813,
|
||||||
|
"spec_mae": 0.5158333333333334,
|
||||||
|
"spec_kripp_alpha": 0.559449580800123,
|
||||||
|
"num_samples": 1200,
|
||||||
|
"total_time_s": 0.0,
|
||||||
|
"avg_ms_per_sample": 0.001,
|
||||||
|
"combined_macro_f1": 0.6058643826501533
|
||||||
|
},
|
||||||
|
"dictionary_vs_Opus-4.6": {
|
||||||
|
"cat_macro_f1": 0.5404608035704013,
|
||||||
|
"cat_weighted_f1": 0.5680942824830456,
|
||||||
|
"cat_macro_precision": 0.564206294840196,
|
||||||
|
"cat_macro_recall": 0.5502937128850568,
|
||||||
|
"cat_mcc": 0.49808632770596933,
|
||||||
|
"cat_auc": 0.7391875463755565,
|
||||||
|
"cat_ece": 0.43000000000000005,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
[
|
||||||
|
162,
|
||||||
|
1,
|
||||||
|
22,
|
||||||
|
3,
|
||||||
|
21,
|
||||||
|
1,
|
||||||
|
4
|
||||||
|
],
|
||||||
|
[
|
||||||
|
1,
|
||||||
|
37,
|
||||||
|
2,
|
||||||
|
8,
|
||||||
|
16,
|
||||||
|
6,
|
||||||
|
9
|
||||||
|
],
|
||||||
|
[
|
||||||
|
20,
|
||||||
|
1,
|
||||||
|
85,
|
||||||
|
6,
|
||||||
|
37,
|
||||||
|
1,
|
||||||
|
8
|
||||||
|
],
|
||||||
|
[
|
||||||
|
3,
|
||||||
|
32,
|
||||||
|
0,
|
||||||
|
29,
|
||||||
|
46,
|
||||||
|
14,
|
||||||
|
17
|
||||||
|
],
|
||||||
|
[
|
||||||
|
22,
|
||||||
|
12,
|
||||||
|
10,
|
||||||
|
7,
|
||||||
|
97,
|
||||||
|
0,
|
||||||
|
65
|
||||||
|
],
|
||||||
|
[
|
||||||
|
2,
|
||||||
|
21,
|
||||||
|
0,
|
||||||
|
5,
|
||||||
|
34,
|
||||||
|
133,
|
||||||
|
33
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
4,
|
||||||
|
0,
|
||||||
|
18,
|
||||||
|
2,
|
||||||
|
2,
|
||||||
|
141
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"cat_f1_BoardGov": 0.7641509433962265,
|
||||||
|
"cat_prec_BoardGov": 0.7714285714285715,
|
||||||
|
"cat_recall_BoardGov": 0.7570093457943925,
|
||||||
|
"cat_f1_Incident": 0.39572192513368987,
|
||||||
|
"cat_prec_Incident": 0.3425925925925926,
|
||||||
|
"cat_recall_Incident": 0.46835443037974683,
|
||||||
|
"cat_f1_Manageme": 0.6137184115523465,
|
||||||
|
"cat_prec_Manageme": 0.7142857142857143,
|
||||||
|
"cat_recall_Manageme": 0.5379746835443038,
|
||||||
|
"cat_f1_NoneOthe": 0.2672811059907834,
|
||||||
|
"cat_prec_NoneOthe": 0.3815789473684211,
|
||||||
|
"cat_recall_NoneOthe": 0.20567375886524822,
|
||||||
|
"cat_f1_RiskMana": 0.41630901287553645,
|
||||||
|
"cat_prec_RiskMana": 0.383399209486166,
|
||||||
|
"cat_recall_RiskMana": 0.45539906103286387,
|
||||||
|
"cat_f1_Strategy": 0.6909090909090909,
|
||||||
|
"cat_prec_Strategy": 0.8471337579617835,
|
||||||
|
"cat_recall_Strategy": 0.5833333333333334,
|
||||||
|
"cat_f1_Third-Pa": 0.6351351351351351,
|
||||||
|
"cat_prec_Third-Pa": 0.5090252707581228,
|
||||||
|
"cat_recall_Third-Pa": 0.844311377245509,
|
||||||
|
"cat_kripp_alpha": 0.49046948704650417,
|
||||||
|
"spec_macro_f1": 0.6345038647761864,
|
||||||
|
"spec_weighted_f1": 0.6901912617666649,
|
||||||
|
"spec_macro_precision": 0.7050601461353045,
|
||||||
|
"spec_macro_recall": 0.6128856912762208,
|
||||||
|
"spec_mcc": 0.5373481008745777,
|
||||||
|
"spec_auc": 0.7435001662825611,
|
||||||
|
"spec_ece": 0.29666666666666663,
|
||||||
|
"spec_confusion_matrix": [
|
||||||
|
[
|
||||||
|
542,
|
||||||
|
33,
|
||||||
|
3,
|
||||||
|
27
|
||||||
|
],
|
||||||
|
[
|
||||||
|
66,
|
||||||
|
73,
|
||||||
|
1,
|
||||||
|
5
|
||||||
|
],
|
||||||
|
[
|
||||||
|
121,
|
||||||
|
26,
|
||||||
|
108,
|
||||||
|
5
|
||||||
|
],
|
||||||
|
[
|
||||||
|
35,
|
||||||
|
22,
|
||||||
|
12,
|
||||||
|
121
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"spec_f1_L1Generi": 0.7918188458729,
|
||||||
|
"spec_prec_L1Generi": 0.7094240837696335,
|
||||||
|
"spec_recall_L1Generi": 0.8958677685950414,
|
||||||
|
"spec_f1_L2Domain": 0.4882943143812709,
|
||||||
|
"spec_prec_L2Domain": 0.474025974025974,
|
||||||
|
"spec_recall_L2Domain": 0.503448275862069,
|
||||||
|
"spec_f1_L3Firm-S": 0.5625,
|
||||||
|
"spec_prec_L3Firm-S": 0.8709677419354839,
|
||||||
|
"spec_recall_L3Firm-S": 0.4153846153846154,
|
||||||
|
"spec_f1_L4Quanti": 0.6954022988505747,
|
||||||
|
"spec_prec_L4Quanti": 0.7658227848101266,
|
||||||
|
"spec_recall_L4Quanti": 0.6368421052631579,
|
||||||
|
"spec_qwk": 0.5875343721356554,
|
||||||
|
"spec_mae": 0.5258333333333334,
|
||||||
|
"spec_kripp_alpha": 0.562049085880076,
|
||||||
|
"num_samples": 1200,
|
||||||
|
"total_time_s": 0.0,
|
||||||
|
"avg_ms_per_sample": 0.001,
|
||||||
|
"combined_macro_f1": 0.5874823341732938
|
||||||
|
}
|
||||||
|
}
|
||||||
54
results/eval/dictionary-baseline/report_gpt-54.txt
Normal file
54
results/eval/dictionary-baseline/report_gpt-54.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: dictionary-baseline vs GPT-5.4
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 0.00s
|
||||||
|
Avg latency: 0.00ms/sample
|
||||||
|
Throughput: 1000000 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.5563 ✗ (target: 0.80)
|
||||||
|
Weighted F1: 0.5867
|
||||||
|
Macro Prec: 0.5821
|
||||||
|
Macro Recall: 0.5593
|
||||||
|
MCC: 0.5160
|
||||||
|
AUC (OvR): 0.7450
|
||||||
|
ECE: 0.4142
|
||||||
|
Kripp Alpha: 0.5092
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.8045 0.8429 0.7696
|
||||||
|
Incident Disclosure 0.4184 0.3796 0.4659
|
||||||
|
Management Role 0.6171 0.6975 0.5533
|
||||||
|
None/Other 0.3113 0.4342 0.2426
|
||||||
|
Risk Management Process 0.4169 0.3715 0.4747
|
||||||
|
Strategy Integration 0.6825 0.8217 0.5837
|
||||||
|
Third-Party Risk 0.6432 0.5271 0.8249
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.6555 ✗ (target: 0.80)
|
||||||
|
Weighted F1: 0.7095
|
||||||
|
Macro Prec: 0.7204
|
||||||
|
Macro Recall: 0.6226
|
||||||
|
MCC: 0.5555
|
||||||
|
AUC (OvR): 0.7507
|
||||||
|
QWK: 0.5757
|
||||||
|
MAE: 0.5158
|
||||||
|
ECE: 0.2800
|
||||||
|
Kripp Alpha: 0.5594
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.8017 0.7251 0.8964
|
||||||
|
L2: Domain 0.5342 0.5584 0.5119
|
||||||
|
L3: Firm-Specific 0.6284 0.8387 0.5024
|
||||||
|
L4: Quantified 0.6575 0.7595 0.5797
|
||||||
|
|
||||||
|
======================================================================
|
||||||
54
results/eval/dictionary-baseline/report_opus-46.txt
Normal file
54
results/eval/dictionary-baseline/report_opus-46.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: dictionary-baseline vs Opus-4.6
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 0.00s
|
||||||
|
Avg latency: 0.00ms/sample
|
||||||
|
Throughput: 1000000 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.5405 ✗ (target: 0.80)
|
||||||
|
Weighted F1: 0.5681
|
||||||
|
Macro Prec: 0.5642
|
||||||
|
Macro Recall: 0.5503
|
||||||
|
MCC: 0.4981
|
||||||
|
AUC (OvR): 0.7392
|
||||||
|
ECE: 0.4300
|
||||||
|
Kripp Alpha: 0.4905
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.7642 0.7714 0.7570
|
||||||
|
Incident Disclosure 0.3957 0.3426 0.4684
|
||||||
|
Management Role 0.6137 0.7143 0.5380
|
||||||
|
None/Other 0.2673 0.3816 0.2057
|
||||||
|
Risk Management Process 0.4163 0.3834 0.4554
|
||||||
|
Strategy Integration 0.6909 0.8471 0.5833
|
||||||
|
Third-Party Risk 0.6351 0.5090 0.8443
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.6345 ✗ (target: 0.80)
|
||||||
|
Weighted F1: 0.6902
|
||||||
|
Macro Prec: 0.7051
|
||||||
|
Macro Recall: 0.6129
|
||||||
|
MCC: 0.5373
|
||||||
|
AUC (OvR): 0.7435
|
||||||
|
QWK: 0.5875
|
||||||
|
MAE: 0.5258
|
||||||
|
ECE: 0.2967
|
||||||
|
Kripp Alpha: 0.5620
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.7918 0.7094 0.8959
|
||||||
|
L2: Domain 0.4883 0.4740 0.5034
|
||||||
|
L3: Firm-Specific 0.5625 0.8710 0.4154
|
||||||
|
L4: Quantified 0.6954 0.7658 0.6368
|
||||||
|
|
||||||
|
======================================================================
|
||||||
@ -0,0 +1,22 @@
|
|||||||
|
{
|
||||||
|
"T_cat": 1.764438052305923,
|
||||||
|
"T_spec": 2.4588486682973603,
|
||||||
|
"per_benchmark": {
|
||||||
|
"GPT-5.4": {
|
||||||
|
"ece_cat_pre": 0.05087702547510463,
|
||||||
|
"ece_cat_post": 0.03403335139155388,
|
||||||
|
"ece_spec_pre": 0.06921947295467064,
|
||||||
|
"ece_spec_post": 0.041827132950226435,
|
||||||
|
"cat_preds_preserved": true,
|
||||||
|
"spec_preds_preserved": false
|
||||||
|
},
|
||||||
|
"Opus-4.6": {
|
||||||
|
"ece_cat_pre": 0.06293055539329852,
|
||||||
|
"ece_cat_post": 0.04372739652792611,
|
||||||
|
"ece_spec_pre": 0.08450941021243728,
|
||||||
|
"ece_spec_post": 0.05213142380118366,
|
||||||
|
"cat_preds_preserved": true,
|
||||||
|
"spec_preds_preserved": false
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
BIN
results/eval/ensemble-3seed/figures/calibration_cat_gpt-5.4.png
Normal file
BIN
results/eval/ensemble-3seed/figures/calibration_cat_gpt-5.4.png
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|
After Width: | Height: | Size: 52 KiB |
BIN
results/eval/ensemble-3seed/figures/calibration_cat_opus-4.6.png
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|
After Width: | Height: | Size: 53 KiB |
BIN
results/eval/ensemble-3seed/figures/confusion_cat_gpt-5.4.png
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|
After Width: | Height: | Size: 119 KiB |
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results/eval/ensemble-3seed/figures/confusion_cat_opus-4.6.png
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|
After Width: | Height: | Size: 120 KiB |
BIN
results/eval/ensemble-3seed/figures/confusion_spec_gpt-5.4.png
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|
After Width: | Height: | Size: 83 KiB |
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results/eval/ensemble-3seed/figures/confusion_spec_opus-4.6.png
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|
After Width: | Height: | Size: 84 KiB |
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results/eval/ensemble-3seed/figures/model_comparison.png
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|
After Width: | Height: | Size: 66 KiB |
BIN
results/eval/ensemble-3seed/figures/per_class_f1_gpt-5.4.png
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|
After Width: | Height: | Size: 105 KiB |
BIN
results/eval/ensemble-3seed/figures/per_class_f1_opus-4.6.png
Normal file
BIN
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|
After Width: | Height: | Size: 106 KiB |
BIN
results/eval/ensemble-3seed/figures/speed_comparison.png
Normal file
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|
After Width: | Height: | Size: 54 KiB |
298
results/eval/ensemble-3seed/metrics.json
Normal file
298
results/eval/ensemble-3seed/metrics.json
Normal file
@ -0,0 +1,298 @@
|
|||||||
|
{
|
||||||
|
"ensemble-3seed_vs_GPT-5.4": {
|
||||||
|
"cat_macro_f1": 0.9382530391727061,
|
||||||
|
"cat_weighted_f1": 0.9385858996685268,
|
||||||
|
"cat_macro_precision": 0.937038491784886,
|
||||||
|
"cat_macro_recall": 0.9417984783962936,
|
||||||
|
"cat_mcc": 0.9275970467019695,
|
||||||
|
"cat_auc": 0.9930606345789074,
|
||||||
|
"cat_ece": 0.05087702547510463,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
[
|
||||||
|
225,
|
||||||
|
0,
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
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|
||||||
|
85,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
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|
||||||
|
1,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
2,
|
||||||
|
0,
|
||||||
|
145,
|
||||||
|
1,
|
||||||
|
2,
|
||||||
|
0,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
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|
||||||
|
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|
||||||
|
0,
|
||||||
|
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|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
6,
|
||||||
|
1,
|
||||||
|
4,
|
||||||
|
18,
|
||||||
|
167,
|
||||||
|
1,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
2,
|
||||||
|
1,
|
||||||
|
8,
|
||||||
|
2,
|
||||||
|
208,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
13,
|
||||||
|
0,
|
||||||
|
164
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"cat_f1_BoardGov": 0.9719222462203023,
|
||||||
|
"cat_prec_BoardGov": 0.9656652360515021,
|
||||||
|
"cat_recall_BoardGov": 0.9782608695652174,
|
||||||
|
"cat_f1_Incident": 0.9659090909090909,
|
||||||
|
"cat_prec_Incident": 0.9659090909090909,
|
||||||
|
"cat_recall_Incident": 0.9659090909090909,
|
||||||
|
"cat_f1_Manageme": 0.9477124183006536,
|
||||||
|
"cat_prec_Manageme": 0.9294871794871795,
|
||||||
|
"cat_recall_Manageme": 0.9666666666666667,
|
||||||
|
"cat_f1_NoneOthe": 0.8949152542372881,
|
||||||
|
"cat_prec_NoneOthe": 0.8301886792452831,
|
||||||
|
"cat_recall_NoneOthe": 0.9705882352941176,
|
||||||
|
"cat_f1_RiskMana": 0.8652849740932642,
|
||||||
|
"cat_prec_RiskMana": 0.8882978723404256,
|
||||||
|
"cat_recall_RiskMana": 0.8434343434343434,
|
||||||
|
"cat_f1_Strategy": 0.9629629629629629,
|
||||||
|
"cat_prec_Strategy": 0.985781990521327,
|
||||||
|
"cat_recall_Strategy": 0.9411764705882353,
|
||||||
|
"cat_f1_Third-Pa": 0.9590643274853801,
|
||||||
|
"cat_prec_Third-Pa": 0.9939393939393939,
|
||||||
|
"cat_recall_Third-Pa": 0.9265536723163842,
|
||||||
|
"cat_kripp_alpha": 0.9272644584249223,
|
||||||
|
"spec_macro_f1": 0.902152688639083,
|
||||||
|
"spec_weighted_f1": 0.9177972939099285,
|
||||||
|
"spec_macro_precision": 0.9070378979232232,
|
||||||
|
"spec_macro_recall": 0.8991005681856252,
|
||||||
|
"spec_mcc": 0.8753613597836426,
|
||||||
|
"spec_auc": 0.9826044267990239,
|
||||||
|
"spec_ece": 0.06921947295467064,
|
||||||
|
"spec_confusion_matrix": [
|
||||||
|
[
|
||||||
|
583,
|
||||||
|
17,
|
||||||
|
15,
|
||||||
|
3
|
||||||
|
],
|
||||||
|
[
|
||||||
|
28,
|
||||||
|
130,
|
||||||
|
9,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
[
|
||||||
|
10,
|
||||||
|
3,
|
||||||
|
192,
|
||||||
|
2
|
||||||
|
],
|
||||||
|
[
|
||||||
|
2,
|
||||||
|
1,
|
||||||
|
7,
|
||||||
|
197
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"spec_f1_L1Generi": 0.9395648670427075,
|
||||||
|
"spec_prec_L1Generi": 0.9357945425361156,
|
||||||
|
"spec_recall_L1Generi": 0.9433656957928802,
|
||||||
|
"spec_f1_L2Domain": 0.8150470219435737,
|
||||||
|
"spec_prec_L2Domain": 0.8609271523178808,
|
||||||
|
"spec_recall_L2Domain": 0.7738095238095238,
|
||||||
|
"spec_f1_L3Firm-S": 0.8930232558139535,
|
||||||
|
"spec_prec_L3Firm-S": 0.8609865470852018,
|
||||||
|
"spec_recall_L3Firm-S": 0.927536231884058,
|
||||||
|
"spec_f1_L4Quanti": 0.9609756097560975,
|
||||||
|
"spec_prec_L4Quanti": 0.9704433497536946,
|
||||||
|
"spec_recall_L4Quanti": 0.9516908212560387,
|
||||||
|
"spec_qwk": 0.9338562415243872,
|
||||||
|
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|
||||||
|
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|
||||||
|
"total_time_s": 19.849480003875215,
|
||||||
|
"num_samples": 1200,
|
||||||
|
"avg_ms_per_sample": 16.54123333656268,
|
||||||
|
"combined_macro_f1": 0.9202028639058946
|
||||||
|
},
|
||||||
|
"ensemble-3seed_vs_Opus-4.6": {
|
||||||
|
"cat_macro_f1": 0.9287535853888995,
|
||||||
|
"cat_weighted_f1": 0.9277067129478959,
|
||||||
|
"cat_macro_precision": 0.9242877868683518,
|
||||||
|
"cat_macro_recall": 0.9368327500295983,
|
||||||
|
"cat_mcc": 0.9160728021840298,
|
||||||
|
"cat_auc": 0.9947981532709612,
|
||||||
|
"cat_ece": 0.06293055539329852,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
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|
||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
|
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||||||
|
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|
||||||
|
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||||||
|
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||||||
|
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||||||
|
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||||||
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||||||
|
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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||||||
|
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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||||||
|
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||||||
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||||||
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||||||
|
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||||||
|
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|
||||||
|
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"num_samples": 1200,
|
||||||
|
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|
||||||
|
"combined_macro_f1": 0.9070206156562974
|
||||||
|
}
|
||||||
|
}
|
||||||
54
results/eval/ensemble-3seed/report_gpt-54.txt
Normal file
54
results/eval/ensemble-3seed/report_gpt-54.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: ensemble-3seed vs GPT-5.4
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 19.85s
|
||||||
|
Avg latency: 16.54ms/sample
|
||||||
|
Throughput: 60 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9383 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9386
|
||||||
|
Macro Prec: 0.9370
|
||||||
|
Macro Recall: 0.9418
|
||||||
|
MCC: 0.9276
|
||||||
|
AUC (OvR): 0.9931
|
||||||
|
ECE: 0.0509
|
||||||
|
Kripp Alpha: 0.9273
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.9719 0.9657 0.9783
|
||||||
|
Incident Disclosure 0.9659 0.9659 0.9659
|
||||||
|
Management Role 0.9477 0.9295 0.9667
|
||||||
|
None/Other 0.8949 0.8302 0.9706
|
||||||
|
Risk Management Process 0.8653 0.8883 0.8434
|
||||||
|
Strategy Integration 0.9630 0.9858 0.9412
|
||||||
|
Third-Party Risk 0.9591 0.9939 0.9266
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9022 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9178
|
||||||
|
Macro Prec: 0.9070
|
||||||
|
Macro Recall: 0.8991
|
||||||
|
MCC: 0.8754
|
||||||
|
AUC (OvR): 0.9826
|
||||||
|
QWK: 0.9339
|
||||||
|
MAE: 0.1125
|
||||||
|
ECE: 0.0692
|
||||||
|
Kripp Alpha: 0.9206
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.9396 0.9358 0.9434
|
||||||
|
L2: Domain 0.8150 0.8609 0.7738
|
||||||
|
L3: Firm-Specific 0.8930 0.8610 0.9275
|
||||||
|
L4: Quantified 0.9610 0.9704 0.9517
|
||||||
|
|
||||||
|
======================================================================
|
||||||
54
results/eval/ensemble-3seed/report_opus-46.txt
Normal file
54
results/eval/ensemble-3seed/report_opus-46.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: ensemble-3seed vs Opus-4.6
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 19.85s
|
||||||
|
Avg latency: 16.54ms/sample
|
||||||
|
Throughput: 60 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9288 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9277
|
||||||
|
Macro Prec: 0.9243
|
||||||
|
Macro Recall: 0.9368
|
||||||
|
MCC: 0.9161
|
||||||
|
AUC (OvR): 0.9948
|
||||||
|
ECE: 0.0629
|
||||||
|
Kripp Alpha: 0.9154
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.9441 0.9056 0.9860
|
||||||
|
Incident Disclosure 0.9341 0.8864 0.9873
|
||||||
|
Management Role 0.9236 0.9295 0.9177
|
||||||
|
None/Other 0.9267 0.8742 0.9858
|
||||||
|
Risk Management Process 0.8628 0.9202 0.8122
|
||||||
|
Strategy Integration 0.9522 0.9905 0.9167
|
||||||
|
Third-Party Risk 0.9578 0.9636 0.9521
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.8853 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9024
|
||||||
|
Macro Prec: 0.8881
|
||||||
|
Macro Recall: 0.8858
|
||||||
|
MCC: 0.8535
|
||||||
|
AUC (OvR): 0.9776
|
||||||
|
QWK: 0.9248
|
||||||
|
MAE: 0.1325
|
||||||
|
ECE: 0.0845
|
||||||
|
Kripp Alpha: 0.9110
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.9300 0.9165 0.9438
|
||||||
|
L2: Domain 0.7973 0.7815 0.8138
|
||||||
|
L3: Firm-Specific 0.8571 0.9283 0.7962
|
||||||
|
L4: Quantified 0.9567 0.9261 0.9895
|
||||||
|
|
||||||
|
======================================================================
|
||||||
BIN
results/eval/iter1-nofilter/figures/calibration_cat_gpt-5.4.png
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|
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BIN
results/eval/iter1-nofilter/figures/calibration_cat_opus-4.6.png
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|
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BIN
results/eval/iter1-nofilter/figures/confusion_cat_gpt-5.4.png
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|
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|
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results/eval/iter1-nofilter/figures/confusion_spec_opus-4.6.png
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|
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results/eval/iter1-nofilter/figures/model_comparison.png
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|
After Width: | Height: | Size: 61 KiB |
BIN
results/eval/iter1-nofilter/figures/per_class_f1_gpt-5.4.png
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|
After Width: | Height: | Size: 103 KiB |
BIN
results/eval/iter1-nofilter/figures/per_class_f1_opus-4.6.png
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|
After Width: | Height: | Size: 104 KiB |
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|
After Width: | Height: | Size: 51 KiB |
298
results/eval/iter1-nofilter/metrics.json
Normal file
298
results/eval/iter1-nofilter/metrics.json
Normal file
@ -0,0 +1,298 @@
|
|||||||
|
{
|
||||||
|
"iter1-nofilter_vs_GPT-5.4": {
|
||||||
|
"cat_macro_f1": 0.9330686485658707,
|
||||||
|
"cat_weighted_f1": 0.9343658185935377,
|
||||||
|
"cat_macro_precision": 0.9322935427373933,
|
||||||
|
"cat_macro_recall": 0.9363353853942956,
|
||||||
|
"cat_mcc": 0.9226928699698839,
|
||||||
|
"cat_auc": 0.9932042643591733,
|
||||||
|
"cat_ece": 0.05255412861704832,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
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|
||||||
|
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||||||
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||||||
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||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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||||||
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|
||||||
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|
||||||
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|
||||||
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||||||
|
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|
||||||
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||||||
|
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|
||||||
|
[
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
[
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
0
|
||||||
|
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|
||||||
|
[
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
163
|
||||||
|
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|
||||||
|
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|
||||||
|
"cat_f1_BoardGov": 0.9741379310344828,
|
||||||
|
"cat_prec_BoardGov": 0.9658119658119658,
|
||||||
|
"cat_recall_BoardGov": 0.9826086956521739,
|
||||||
|
"cat_f1_Incident": 0.9545454545454546,
|
||||||
|
"cat_prec_Incident": 0.9545454545454546,
|
||||||
|
"cat_recall_Incident": 0.9545454545454546,
|
||||||
|
"cat_f1_Manageme": 0.9403973509933775,
|
||||||
|
"cat_prec_Manageme": 0.9342105263157895,
|
||||||
|
"cat_recall_Manageme": 0.9466666666666667,
|
||||||
|
"cat_f1_NoneOthe": 0.8888888888888888,
|
||||||
|
"cat_prec_NoneOthe": 0.8198757763975155,
|
||||||
|
"cat_recall_NoneOthe": 0.9705882352941176,
|
||||||
|
"cat_f1_RiskMana": 0.859375,
|
||||||
|
"cat_prec_RiskMana": 0.8870967741935484,
|
||||||
|
"cat_recall_RiskMana": 0.8333333333333334,
|
||||||
|
"cat_f1_Strategy": 0.960919540229885,
|
||||||
|
"cat_prec_Strategy": 0.9766355140186916,
|
||||||
|
"cat_recall_Strategy": 0.9457013574660633,
|
||||||
|
"cat_f1_Third-Pa": 0.9532163742690059,
|
||||||
|
"cat_prec_Third-Pa": 0.9878787878787879,
|
||||||
|
"cat_recall_Third-Pa": 0.9209039548022598,
|
||||||
|
"cat_kripp_alpha": 0.9223381216103527,
|
||||||
|
"spec_macro_f1": 0.9014230599860553,
|
||||||
|
"spec_weighted_f1": 0.9156317347190472,
|
||||||
|
"spec_macro_precision": 0.903753901233204,
|
||||||
|
"spec_macro_recall": 0.9008573036643952,
|
||||||
|
"spec_mcc": 0.8719529896272543,
|
||||||
|
"spec_auc": 0.980550012888276,
|
||||||
|
"spec_ece": 0.07280499959985415,
|
||||||
|
"spec_confusion_matrix": [
|
||||||
|
[
|
||||||
|
577,
|
||||||
|
19,
|
||||||
|
20,
|
||||||
|
2
|
||||||
|
],
|
||||||
|
[
|
||||||
|
26,
|
||||||
|
132,
|
||||||
|
9,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
[
|
||||||
|
11,
|
||||||
|
2,
|
||||||
|
192,
|
||||||
|
2
|
||||||
|
],
|
||||||
|
[
|
||||||
|
2,
|
||||||
|
1,
|
||||||
|
6,
|
||||||
|
198
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"spec_f1_L1Generi": 0.9351701782820098,
|
||||||
|
"spec_prec_L1Generi": 0.9366883116883117,
|
||||||
|
"spec_recall_L1Generi": 0.9336569579288025,
|
||||||
|
"spec_f1_L2Domain": 0.8198757763975155,
|
||||||
|
"spec_prec_L2Domain": 0.8571428571428571,
|
||||||
|
"spec_recall_L2Domain": 0.7857142857142857,
|
||||||
|
"spec_f1_L3Firm-S": 0.8847926267281107,
|
||||||
|
"spec_prec_L3Firm-S": 0.8458149779735683,
|
||||||
|
"spec_recall_L3Firm-S": 0.927536231884058,
|
||||||
|
"spec_f1_L4Quanti": 0.9658536585365853,
|
||||||
|
"spec_prec_L4Quanti": 0.9753694581280788,
|
||||||
|
"spec_recall_L4Quanti": 0.9565217391304348,
|
||||||
|
"spec_qwk": 0.9298651869833414,
|
||||||
|
"spec_mae": 0.11833333333333333,
|
||||||
|
"spec_kripp_alpha": 0.9154486849160884,
|
||||||
|
"total_time_s": 6.824244472139981,
|
||||||
|
"num_samples": 1200,
|
||||||
|
"avg_ms_per_sample": 5.686870393449984,
|
||||||
|
"combined_macro_f1": 0.917245854275963
|
||||||
|
},
|
||||||
|
"iter1-nofilter_vs_Opus-4.6": {
|
||||||
|
"cat_macro_f1": 0.9234237131691513,
|
||||||
|
"cat_weighted_f1": 0.9225818680324113,
|
||||||
|
"cat_macro_precision": 0.9194178999323832,
|
||||||
|
"cat_macro_recall": 0.9313952755342539,
|
||||||
|
"cat_mcc": 0.9102188510350809,
|
||||||
|
"cat_auc": 0.9942333075075134,
|
||||||
|
"cat_ece": 0.06428046062588692,
|
||||||
|
"cat_confusion_matrix": [
|
||||||
|
[
|
||||||
|
211,
|
||||||
|
0,
|
||||||
|
1,
|
||||||
|
2,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
78,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
1,
|
||||||
|
0,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
9,
|
||||||
|
0,
|
||||||
|
140,
|
||||||
|
3,
|
||||||
|
6,
|
||||||
|
0,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
1,
|
||||||
|
138,
|
||||||
|
1,
|
||||||
|
1,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
13,
|
||||||
|
1,
|
||||||
|
9,
|
||||||
|
14,
|
||||||
|
170,
|
||||||
|
1,
|
||||||
|
5
|
||||||
|
],
|
||||||
|
[
|
||||||
|
1,
|
||||||
|
9,
|
||||||
|
1,
|
||||||
|
4,
|
||||||
|
2,
|
||||||
|
211,
|
||||||
|
0
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
0,
|
||||||
|
6,
|
||||||
|
1,
|
||||||
|
160
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"cat_f1_BoardGov": 0.9419642857142857,
|
||||||
|
"cat_prec_BoardGov": 0.9017094017094017,
|
||||||
|
"cat_recall_BoardGov": 0.985981308411215,
|
||||||
|
"cat_f1_Incident": 0.9341317365269461,
|
||||||
|
"cat_prec_Incident": 0.8863636363636364,
|
||||||
|
"cat_recall_Incident": 0.9873417721518988,
|
||||||
|
"cat_f1_Manageme": 0.9032258064516129,
|
||||||
|
"cat_prec_Manageme": 0.9210526315789473,
|
||||||
|
"cat_recall_Manageme": 0.8860759493670886,
|
||||||
|
"cat_f1_NoneOthe": 0.9139072847682119,
|
||||||
|
"cat_prec_NoneOthe": 0.8571428571428571,
|
||||||
|
"cat_recall_NoneOthe": 0.9787234042553191,
|
||||||
|
"cat_f1_RiskMana": 0.8521303258145363,
|
||||||
|
"cat_prec_RiskMana": 0.9139784946236559,
|
||||||
|
"cat_recall_RiskMana": 0.7981220657276995,
|
||||||
|
"cat_f1_Strategy": 0.9547511312217195,
|
||||||
|
"cat_prec_Strategy": 0.985981308411215,
|
||||||
|
"cat_recall_Strategy": 0.9254385964912281,
|
||||||
|
"cat_f1_Third-Pa": 0.963855421686747,
|
||||||
|
"cat_prec_Third-Pa": 0.9696969696969697,
|
||||||
|
"cat_recall_Third-Pa": 0.9580838323353293,
|
||||||
|
"cat_kripp_alpha": 0.9095331843779679,
|
||||||
|
"spec_macro_f1": 0.8808130644802126,
|
||||||
|
"spec_weighted_f1": 0.8984641049705442,
|
||||||
|
"spec_macro_precision": 0.8807668956442312,
|
||||||
|
"spec_macro_recall": 0.8837394559738232,
|
||||||
|
"spec_mcc": 0.8473945294385262,
|
||||||
|
"spec_auc": 0.9733956269476784,
|
||||||
|
"spec_ece": 0.09021254365642863,
|
||||||
|
"spec_confusion_matrix": [
|
||||||
|
[
|
||||||
|
566,
|
||||||
|
25,
|
||||||
|
13,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
[
|
||||||
|
20,
|
||||||
|
118,
|
||||||
|
6,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
[
|
||||||
|
30,
|
||||||
|
10,
|
||||||
|
207,
|
||||||
|
13
|
||||||
|
],
|
||||||
|
[
|
||||||
|
0,
|
||||||
|
1,
|
||||||
|
1,
|
||||||
|
188
|
||||||
|
]
|
||||||
|
],
|
||||||
|
"spec_f1_L1Generi": 0.9271089271089271,
|
||||||
|
"spec_prec_L1Generi": 0.9188311688311688,
|
||||||
|
"spec_recall_L1Generi": 0.9355371900826446,
|
||||||
|
"spec_f1_L2Domain": 0.7892976588628763,
|
||||||
|
"spec_prec_L2Domain": 0.7662337662337663,
|
||||||
|
"spec_recall_L2Domain": 0.8137931034482758,
|
||||||
|
"spec_f1_L3Firm-S": 0.8501026694045175,
|
||||||
|
"spec_prec_L3Firm-S": 0.9118942731277533,
|
||||||
|
"spec_recall_L3Firm-S": 0.7961538461538461,
|
||||||
|
"spec_f1_L4Quanti": 0.9567430025445293,
|
||||||
|
"spec_prec_L4Quanti": 0.9261083743842364,
|
||||||
|
"spec_recall_L4Quanti": 0.9894736842105263,
|
||||||
|
"spec_qwk": 0.9194878532889771,
|
||||||
|
"spec_mae": 0.14,
|
||||||
|
"spec_kripp_alpha": 0.9062176873986938,
|
||||||
|
"total_time_s": 6.824244472139981,
|
||||||
|
"num_samples": 1200,
|
||||||
|
"avg_ms_per_sample": 5.686870393449984,
|
||||||
|
"combined_macro_f1": 0.902118388824682
|
||||||
|
}
|
||||||
|
}
|
||||||
54
results/eval/iter1-nofilter/report_gpt-54.txt
Normal file
54
results/eval/iter1-nofilter/report_gpt-54.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: iter1-nofilter vs GPT-5.4
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 6.82s
|
||||||
|
Avg latency: 5.69ms/sample
|
||||||
|
Throughput: 176 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9331 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9344
|
||||||
|
Macro Prec: 0.9323
|
||||||
|
Macro Recall: 0.9363
|
||||||
|
MCC: 0.9227
|
||||||
|
AUC (OvR): 0.9932
|
||||||
|
ECE: 0.0526
|
||||||
|
Kripp Alpha: 0.9223
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.9741 0.9658 0.9826
|
||||||
|
Incident Disclosure 0.9545 0.9545 0.9545
|
||||||
|
Management Role 0.9404 0.9342 0.9467
|
||||||
|
None/Other 0.8889 0.8199 0.9706
|
||||||
|
Risk Management Process 0.8594 0.8871 0.8333
|
||||||
|
Strategy Integration 0.9609 0.9766 0.9457
|
||||||
|
Third-Party Risk 0.9532 0.9879 0.9209
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9014 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9156
|
||||||
|
Macro Prec: 0.9038
|
||||||
|
Macro Recall: 0.9009
|
||||||
|
MCC: 0.8720
|
||||||
|
AUC (OvR): 0.9806
|
||||||
|
QWK: 0.9299
|
||||||
|
MAE: 0.1183
|
||||||
|
ECE: 0.0728
|
||||||
|
Kripp Alpha: 0.9154
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.9352 0.9367 0.9337
|
||||||
|
L2: Domain 0.8199 0.8571 0.7857
|
||||||
|
L3: Firm-Specific 0.8848 0.8458 0.9275
|
||||||
|
L4: Quantified 0.9659 0.9754 0.9565
|
||||||
|
|
||||||
|
======================================================================
|
||||||
54
results/eval/iter1-nofilter/report_opus-46.txt
Normal file
54
results/eval/iter1-nofilter/report_opus-46.txt
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
|
||||||
|
======================================================================
|
||||||
|
HOLDOUT EVALUATION: iter1-nofilter vs Opus-4.6
|
||||||
|
======================================================================
|
||||||
|
|
||||||
|
Samples evaluated: 1200
|
||||||
|
Total inference time: 6.82s
|
||||||
|
Avg latency: 5.69ms/sample
|
||||||
|
Throughput: 176 samples/sec
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
CATEGORY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.9234 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.9226
|
||||||
|
Macro Prec: 0.9194
|
||||||
|
Macro Recall: 0.9314
|
||||||
|
MCC: 0.9102
|
||||||
|
AUC (OvR): 0.9942
|
||||||
|
ECE: 0.0643
|
||||||
|
Kripp Alpha: 0.9095
|
||||||
|
|
||||||
|
Category F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
Board Governance 0.9420 0.9017 0.9860
|
||||||
|
Incident Disclosure 0.9341 0.8864 0.9873
|
||||||
|
Management Role 0.9032 0.9211 0.8861
|
||||||
|
None/Other 0.9139 0.8571 0.9787
|
||||||
|
Risk Management Process 0.8521 0.9140 0.7981
|
||||||
|
Strategy Integration 0.9548 0.9860 0.9254
|
||||||
|
Third-Party Risk 0.9639 0.9697 0.9581
|
||||||
|
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
SPECIFICITY CLASSIFICATION
|
||||||
|
──────────────────────────────────────────────────
|
||||||
|
Macro F1: 0.8808 ✓ (target: 0.80)
|
||||||
|
Weighted F1: 0.8985
|
||||||
|
Macro Prec: 0.8808
|
||||||
|
Macro Recall: 0.8837
|
||||||
|
MCC: 0.8474
|
||||||
|
AUC (OvR): 0.9734
|
||||||
|
QWK: 0.9195
|
||||||
|
MAE: 0.1400
|
||||||
|
ECE: 0.0902
|
||||||
|
Kripp Alpha: 0.9062
|
||||||
|
|
||||||
|
Level F1 Prec Recall
|
||||||
|
------------------------- -------- -------- --------
|
||||||
|
L1: Generic 0.9271 0.9188 0.9355
|
||||||
|
L2: Domain 0.7893 0.7662 0.8138
|
||||||
|
L3: Firm-Specific 0.8501 0.9119 0.7962
|
||||||
|
L4: Quantified 0.9567 0.9261 0.9895
|
||||||
|
|
||||||
|
======================================================================
|
||||||
Loading…
x
Reference in New Issue
Block a user