diff --git a/analysis/charts.py b/analysis/charts.py index ef033af..e5d7eab 100644 --- a/analysis/charts.py +++ b/analysis/charts.py @@ -55,6 +55,9 @@ def style(ax, grid_axis="x"): ax.set_axisbelow(True) +LABEL = "piloto, 50 exemplos de dev" + + def accuracy_chart(data, dest): rows = sorted(((NAMES[c], s["quality"]["accuracy"] * 100) for (c, d), s in data.items() if d == "cuda"), key=lambda r: r[1]) @@ -64,7 +67,7 @@ def accuracy_chart(data, dest): ax.text(v + 1, bar.get_y() + bar.get_height() / 2, f"{v:.0f}%", va="center", color=INK, fontsize=11) ax.set_xlim(0, 105) ax.set_xlabel("Acurácia (%)") - ax.set_title("Acurácia por candidato — piloto, 50 exemplos de dev", loc="left", fontsize=12, color=INK) + ax.set_title(f"Acurácia por candidato — {LABEL}", loc="left", fontsize=12, color=INK) style(ax) fig.tight_layout() save(fig, dest / "chart-acuracia.svg") @@ -87,7 +90,8 @@ def latency_chart(data, dest): label="GPU (A10)" if i == 0 else None) ax.text(g / 1.18, i, f"{g:.0f} ms", va="center", ha="right", color=INK, fontsize=10) ax.set_xscale("log") - ax.set_xlim(5, 200_000) + top = max(s["latency_ms"]["p50"] for s in data.values()) + ax.set_xlim(5, top * 8) ax.set_yticks(range(len(cands)), [NAMES[c] for c in cands]) ax.set_ylim(-0.6, len(cands) - 0.4) ax.set_xlabel("Latência mediana por decisão (ms, escala log)") @@ -113,13 +117,48 @@ def tradeoff_chart(data, dest): ax.set_ylim(30, 104) ax.set_xlabel("Latência p95 por decisão em GPU A10 (ms)") ax.set_ylabel("Acurácia (%)") - ax.set_title("Qualidade × latência em GPU — piloto", loc="left", fontsize=12, color=INK) + ax.set_title(f"Qualidade × latência em GPU — {LABEL}", loc="left", fontsize=12, color=INK) style(ax, grid_axis="both") fig.tight_layout() save(fig, dest / "chart-qualidade-latencia.svg") plt.close(fig) +def options_chart(dest): + """Acurácia por número de opções (GPU), uma linha por candidato com cor fixa e rótulo direto.""" + from collections import defaultdict + order = [c for c in ("semif", "rizzo_flow", "laya", "gliner") if (dest / f"predictions-{c}-cuda.jsonl").exists()] + if len(order) < 2: + return + fig, ax = plt.subplots(figsize=(7, 3.8)) + ends = [] + for c in order: + byk = defaultdict(list) + for l in (dest / f"predictions-{c}-cuda.jsonl").open(): + r = json.loads(l) + byk[r["n_options"]].append(r["pred"] == r["gold"]) + ks = sorted(byk) + ys = [sum(byk[k]) / len(byk[k]) * 100 for k in ks] + ax.plot(ks, ys, color=ENTITY[c], linewidth=2, marker="o", markersize=7, + markeredgecolor=SURFACE, markeredgewidth=2) + ends.append([ys[-1], c]) + ends.sort() + for i in range(1, len(ends)): # afasta rótulos finais muito próximos + ends[i][0] = max(ends[i][0], ends[i - 1][0] + 4.5) + for y, c in ends: + ax.text(16.5, y, NAMES[c], va="center", color=INK, fontsize=10) + ax.set_xticks([4, 8, 12, 16]) + ax.set_xlim(3, 19.5) + ax.set_ylim(30, 100) + ax.set_xlabel("Opções por pergunta (incluindo sem_correspondencia)") + ax.set_ylabel("Acurácia (%)") + ax.set_title(f"Acurácia por número de opções (GPU) — {LABEL}", loc="left", fontsize=12, color=INK) + style(ax, grid_axis="y") + fig.tight_layout() + save(fig, dest / "chart-acuracia-por-opcoes.svg") + plt.close(fig) + + def calibration_charts(dest): from analysis.calibrate import apply_temperature from analysis.metrics import selective @@ -205,7 +244,10 @@ def calibration_charts(dest): def main(): dest = Path(sys.argv[1]) + global LABEL kind = sys.argv[sys.argv.index("--kind") + 1] if "--kind" in sys.argv else "pilot" + if "--label" in sys.argv: + LABEL = sys.argv[sys.argv.index("--label") + 1] if kind == "calibration": calibration_charts(dest) else: @@ -213,6 +255,7 @@ def main(): accuracy_chart(data, dest) latency_chart(data, dest) tradeoff_chart(data, dest) + options_chart(dest) print(sorted(p.name for p in dest.glob("chart-*.svg"))) diff --git a/docs/03-plano-de-tarefas.md b/docs/03-plano-de-tarefas.md index ba994db..4afa95c 100644 --- a/docs/03-plano-de-tarefas.md +++ b/docs/03-plano-de-tarefas.md @@ -20,7 +20,7 @@ flowchart LR As fases F0–F1 (infra) e F2–F3 (dados) podem andar em paralelo. -**Andamento em 2026-10-07:** 57 de 78 tarefas concluídas (73%), 3 em andamento. +**Andamento em 2026-10-07:** 58 de 78 tarefas concluídas (74%), 2 em andamento. | Fase | Concluídas | Em andamento | Pendentes | |------|-----------|--------------|-----------| @@ -31,11 +31,11 @@ As fases F0–F1 (infra) e F2–F3 (dados) podem andar em paralelo. | F4 — Contrato JEV e adaptadores | 16 | 0 | 0 | | F5 — Controle de execução | 3 | 0 | 0 | | F6 — Piloto | 4 | 0 | 0 | -| F7 — Desenvolvimento e calibração | 3 | 1 | 0 | +| F7 — Desenvolvimento e calibração | 4 | 0 | 0 | | F8 — Teste final | 0 | 0 | 4 | | F9 — Métricas e análise | 0 | 0 | 8 | | F10 — Relatório e encerramento | 0 | 0 | 4 | -| **Total** | **57** | **3** | **18** | +| **Total** | **58** | **2** | **18** | Legenda: `[x]` concluída · `[~]` em andamento ou parcial · `[ ]` pendente. @@ -183,7 +183,8 @@ Legenda: `[x]` concluída · `[~]` em andamento ou parcial · `[ ]` pendente. ## F7 — Desenvolvimento e calibração -- [~] **59.** Executar os candidatos no conjunto de desenvolvimento, sem calibração adicional. +- [x] **59.** Executar os candidatos no conjunto de desenvolvimento, sem calibração adicional. + - ↳ 1.183 exemplos, sem erros: SemIf 88,4%, Rizzo Flow 85,5%, GLiNER 55,5%, Laya 54,0% (GPU). Resultados em [`reports/dev/`](../reports/dev/README.md). - [x] **60.** Escolher os limites de confiança e as regras de abstenção só com a partição de calibração. - ↳ Abster-se significa encaminhar para desambiguação. A regra é escolhida por meta de cobertura ou de risco, definida antes. *(Q5)* - ↳ `analysis/calibrate.py` → `config/calibration.json`: menor limiar com rota errada com confiança alta ≤ 2%. Cobertura na calibração: SemIf 59,5%, Rizzo Flow 47,6%, GLiNER 11,8%, Laya 5,1% (nenhum atinge os 80% do critério H2). diff --git a/reports/dev/README.md b/reports/dev/README.md new file mode 100644 index 0000000..8031b41 --- /dev/null +++ b/reports/dev/README.md @@ -0,0 +1,75 @@ +# Desenvolvimento — partição completa (1.183 exemplos) + +## Sumário executivo + +A rodada de desenvolvimento executou os quatro candidatos sobre toda a partição de dev, sem calibração. É a primeira medição em escala do benchmark e a referência para comparar com a calibração e com o teste final. + +- **Qualidade:** **SemIf 88,4%** e **Rizzo Flow 85,5%** de acurácia, com macro F1 de 90,1% e 88,3%. **GLiNER (55,5%)** e **Laya (54,0%)** ficam cerca de 30 p.p. atrás, e a ordem se mantém em relação ao piloto. +- **Robustez ao número de opções:** SemIf fica estável entre 4 e 16 opções (88–89%); Rizzo varia entre 84% e 89%; o **Laya cai de 70% para 45%** quando as opções passam de 4 para 16. +- **Pedidos sem correspondência:** SemIf reconhece 78% deles; GLiNER, 62%, mas à custa de marcar 242 pedidos válidos como `sem_correspondencia`; Rizzo, 60%; Laya, 28%. +- **Latência:** em GPU A10, Laya e GLiNER ficam em cerca de 20 ms de mediana (p95 de 23 a 31 ms); SemIf e Rizzo, em cerca de 115 ms (p95 de 181 a 187 ms, acima do limite de 150 ms). Em CPU, Laya e GLiNER levam de 180 a 250 ms. +- **Execução:** 1.183 decisões por candidato e dispositivo, sem erros e sem truncamento. + +## Resultados gerais + +| Candidato | Acurácia | Macro F1 | `sem_correspondencia` reconhecido | Acerto dentro do escopo | Pedidos válidos marcados como `sem_correspondencia` | p50 GPU | p95 GPU | +|---|---|---|---|---|---|---|---| +| SemIf | 88,4% | 90,1% | 78,2% | 91,5% | 32 | 117 ms | 187 ms | +| Rizzo Flow | 85,5% | 88,3% | 59,6% | 93,4% | 6 | 112 ms | 181 ms | +| GLiNER | 55,5% | 55,3% | 61,5% | 53,6% | 242 | 18 ms | 31 ms | +| Laya | 54,0% | 56,4% | 28,0% | 61,9% | 37 | 21 ms | 23 ms | + +![Acurácia por candidato](chart-acuracia.svg) + +![Acurácia por número de opções](chart-acuracia-por-opcoes.svg) + +![Qualidade × latência em GPU](chart-qualidade-latencia.svg) + +![Latência por decisão: GPU × CPU](chart-latencia.svg) + +**Acurácia por perfil de escrita (GPU)** + +| Perfil | SemIf | Rizzo Flow | GLiNER | Laya | +|---|---|---|---|---| +| Curto e neutro | 88,2% | 87,5% | 58,3% | 66,0% | +| Informal, com abreviações | 92,7% | 88,5% | 60,4% | 55,7% | +| Erros de digitação | 89,0% | 82,8% | 50,2% | 49,8% | +| Regionalismo | 84,9% | 87,2% | 63,7% | 60,9% | +| Emocional | 92,2% | 85,5% | 47,5% | 45,8% | +| Longo, com contexto | 92,5% | 85,8% | 67,5% | 56,7% | +| Indireto ou com várias intenções | 78,2% | 81,7% | 43,7% | 45,8% | + +## Resultados detalhados + +Probabilidades brutas. Lote 1. As latências excluem os 5 primeiros exemplos (aquecimento). SemIf e Rizzo Flow rodam só em GPU, porque são inviáveis em CPU pela regra do piloto. + + +| Candidato | Disp. | Precisão | n | Acurácia | Macro F1 | ECE (bruto) | p50 ms | p95 ms | p99 ms | Carga s | RAM pico MiB | GPU pico MiB | Erros | Truncados | +|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---| +| gliner | cpu | float32 | 1183 | 55.5% | 55.3% | 0.088 | 252.5 | 478.6 | 586.9 | 20.1 | 3258 | — | 0 | 0 | +| gliner | cuda | float32 | 1183 | 55.5% | 55.3% | 0.088 | 18.4 | 31.4 | 33.5 | 21.8 | 3228 | 1959 | 0 | 0 | +| laya | cpu | float32 | 1183 | 54.4% | 56.8% | 0.287 | 182.3 | 225.6 | 269.5 | 9.6 | 2679 | — | 0 | 0 | +| laya | cuda | float32 | 1183 | 54.0% | 56.4% | 0.290 | 21.3 | 22.8 | 24.0 | 8.6 | 2788 | 1811 | 0 | 0 | +| rizzo_flow | cuda | gguf-q8_0 | 1183 | 85.5% | 88.3% | 0.035 | 111.8 | 180.6 | 184.7 | 6.2 | 4529 | 5115 | 0 | 0 | +| semif | cuda | bfloat16 | 1183 | 88.4% | 90.1% | 0.042 | 117.1 | 186.5 | 190.1 | 7.8 | 9028 | 8513 | 0 | 0 | + + +Arquivos: `summary--.json` (resumo, cenário, precisão e diagnósticos) e `predictions--.jsonl` (uma linha por exemplo, com a distribuição completa de probabilidades e os metadados do exemplo). + +## Metodologia + +**Dados.** A partição de desenvolvimento do conjunto congelado `d1-v1` inteira, sem amostragem: 1.183 exemplos de 99 lotes de geração, com os 43 rótulos. São 178 pedidos fora de escopo, 98 exemplos com a intenção correta fora das opções (rótulo `sem_correspondencia`) e opções por pergunta assim distribuídas: 4 (320), 8 (311), 12 (274) e 16 (278). A partição é disjunta da de calibração e da de teste por lote de geração. + +**Tarefa e execução.** Classificação de intenção (D1) pelo contrato `jev-compat-v1`, com a mesma entrada para todos. Lote 1, precisão nativa de cada candidato e inferência isolada de fontes externas, com artefatos verificados por SHA-256. Nenhuma calibração nem limiar foi aplicado nesta rodada (tarefa 59). + +**Cenário.** GPU `VM.GPU.A10.1` em sa-saopaulo-1 para os quatro candidatos; CPU `VM.Standard.E4.Flex` com 8 OCPU em us-chicago-1 para Laya e GLiNER. Mais detalhes em [docs/05](../../docs/05-cenario-de-execucao.md). + +**Uso desta rodada.** O dev serve de referência e de diagnóstico. Os parâmetros que vão para o teste (temperatura e limiar) vêm exclusivamente da [partição de calibração](../calibration/README.md); nada aqui foi usado para ajustá-los. + +## Conclusão + +1. **Dois grupos bem separados.** Os modelos de 4B (SemIf e Rizzo Flow) ficam entre 85% e 89% de acurácia; os encoders compactos (Laya e GLiNER), entre 54% e 56%. A distância de cerca de 30 p.p. se confirma na escala completa. +2. **O SemIf é o mais robusto.** Mantém a acurácia com qualquer número de opções e em quase todos os perfis de escrita; seu ponto fraco são os pedidos indiretos ou com várias intenções (78%). +3. **O Laya depende de poucas opções.** Com 4 opções, chega a 70%; com 12 ou mais, cai para 45%. É viável só com uma elegibilidade que restrinja bem as opções. +4. **O GLiNER abstém demais.** Reconhece bem os pedidos sem correspondência, mas descarta 242 pedidos válidos (20% do escopo), o que no orquestrador viraria desambiguação desnecessária. +5. **A latência separa os mesmos grupos.** Os encoders cabem com folga no limite de p95 em GPU e são viáveis em CPU; os modelos de 4B, não. diff --git a/reports/dev/chart-acuracia-por-opcoes.svg b/reports/dev/chart-acuracia-por-opcoes.svg new file mode 100644 index 0000000..e401d91 --- /dev/null +++ b/reports/dev/chart-acuracia-por-opcoes.svg @@ -0,0 +1,356 @@ + + + + + + + + 2026-10-07T23:09:50.392478 + image/svg+xml + + + Matplotlib v3.10.7, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + 4 + + + + + + + + + + 8 + + + + + + + + + + 12 + + + + + + + + + + 16 + + + + Opções por pergunta (incluindo sem_correspondencia) + + + + + + + + + + + + + + + + + 30 + + + + + + + + + + + + + 40 + + + + + + + + + + + + + 50 + + + + + + + + + + + + + 60 + + + + + + + + + + + + + 70 + + + + + + + + + + + + + 80 + + + + + + + + + + + + + 90 + + + + + + + + + + + + + 100 + + + + Acurácia (%) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Laya + + + GLiNER + + + Rizzo Flow + + + SemIf + + + Acurácia por número de opções (GPU) — dev completo, 1.183 exemplos + + + + + + + + + diff --git a/reports/dev/chart-acuracia.svg b/reports/dev/chart-acuracia.svg new file mode 100644 index 0000000..77ccbd3 --- /dev/null +++ b/reports/dev/chart-acuracia.svg @@ -0,0 +1,251 @@ + + + + + + + + 2026-10-07T23:09:50.146484 + image/svg+xml + + + Matplotlib v3.10.7, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 20 + + + + + + + + + + + + + 40 + + + + + + + + + + + + + 60 + + + + + + + + + + + + + 80 + + + + + + + + + + + + + 100 + + + + Acurácia (%) + + + + + + + + + + + + + + Laya + + + + + + + + + + GLiNER + + + + + + + + + + Rizzo Flow + + + + + + + + + + SemIf + + + + + + + + + + + + + + + + + + + + + + + 54% + + + 55% + + + 86% + + + 88% + + + Acurácia por candidato — dev completo, 1.183 exemplos + + + + + + + + + diff --git a/reports/dev/chart-latencia.svg b/reports/dev/chart-latencia.svg new file mode 100644 index 0000000..1c5d22a --- /dev/null +++ b/reports/dev/chart-latencia.svg @@ -0,0 +1,444 @@ + + + + + + + + 2026-10-07T23:09:50.243636 + image/svg+xml + + + Matplotlib v3.10.7, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 1 + 0 + 1 + + + + + + + + + + + + + + + + + + 1 + 0 + 2 + + + + + + + + + + + + + + + + + + 1 + 0 + 3 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Latência mediana por decisão (ms, escala log) + + + + + + + + + + + + + + GLiNER + + + + + + + + + + Laya + + + + + + + + + + Rizzo Flow + + + + + + + + + + SemIf + + + + + + + + + + + + + + + + + + + + + + + + + 253 ms + + + + + + + + + + + 18 ms + + + + + + + + 182 ms + + + + + + + + 21 ms + + + + + + + + 112 ms + + + + + + + + 117 ms + + + Latência por decisão: GPU × CPU (lote 1) + + + + + + + + + CPU (8 OCPU) + + + + + + + + GPU (A10) + + + + + + + + + + diff --git a/reports/dev/chart-qualidade-latencia.svg b/reports/dev/chart-qualidade-latencia.svg new file mode 100644 index 0000000..e845bcf --- /dev/null +++ b/reports/dev/chart-qualidade-latencia.svg @@ -0,0 +1,388 @@ + + + + + + + + 2026-10-07T23:09:50.315066 + image/svg+xml + + + Matplotlib v3.10.7, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + + + + + + + + + + + 25 + + + + + + + + + + + + + 50 + + + + + + + + + + + + + 75 + + + + + + + + + + + + + 100 + + + + + + + + + + + + + 125 + + + + + + + + + + + + + 150 + + + + + + + + + + + + + 175 + + + + + + + + + + + + + 200 + + + + Latência p95 por decisão em GPU A10 (ms) + + + + + + + + + + + + + + + + + 30 + + + + + + + + + + + + + 40 + + + + + + + + + + + + + 50 + + + + + + + + + + + + + 60 + + + + + + + + + + + + + 70 + + + + + + + + + + + + + 80 + + + + + + + + + + + + + 90 + + + + + + + + + + + + + 100 + + + + Acurácia (%) + + + + + + + + + + + + + limite p95 = 150 ms + + + + + + + + + + + GLiNER + + + + + + + + Laya + + + + + + + + Rizzo Flow + + + + + + + + SemIf + + + Qualidade × latência em GPU — dev completo, 1.183 exemplos + + + + + + + + + diff --git a/reports/dev/predictions-gliner-cpu.jsonl b/reports/dev/predictions-gliner-cpu.jsonl new file mode 100644 index 0000000..5a58305 --- /dev/null +++ b/reports/dev/predictions-gliner-cpu.jsonl @@ -0,0 +1,1183 @@ +{"id": "g0003-conta_segunda_via-regionalismo-00-D1", "gold": "conta_segunda_via", "warmup": true, "state_tokens": 51, "truncated": false, "n_options": 12, "meta": {"taxonomy_version": "v1.0", "source": "synthetic", "intent_gold": "conta_segunda_via", "gold_eligible": true, "domain": "contas", "kind": "intent", "oos_category": null, "profile": "regionalismo", "sentiment": "neutro", "secondary": [], "ambiguity": "baixa", "annotation": "consenso", "gen_agreement": true, "generator": "cohere.command-a-03-2025", "n_options": 12, "length_chars": 89, "length_bucket": "medio"}, "pred": "conta_segunda_via", "probabilities": {"internet_problema_conexao": 0.028574352355656932, "suporte_configuracao": 0.014710787336386097, "seguranca_fraude": 0.08537924628638698, "conta_explicacao": 0.043427789445137165, "conta_contestacao": 0.05052283234292244, "conta_debito_automatico_digital": 0.004654223284112539, "conta_pagamento_confirmacao": 0.023089761831053626, "conta_segunda_via": 0.6470595855088112, "conta_vencimento": 0.0013527122164697929, "troca_numero": 0.0008892997055374085, "vas_contratacao": 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