Piloto: sumário executivo, gráficos, metodologia e conclusão

- analysis/charts.py: gráficos SVG (acurácia, qualidade × latência, GPU × CPU)
  com paleta validada para daltonismo.
- collect.py regenera só a tabela entre marcadores e preserva o README.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

analysis/charts.py

@@ -0,0 +1,128 @@
+"""Gráficos (SVG) a partir dos resumos coletados em reports/<etapa>/ (tarefa 74).
+
+Paleta: slots 1 (azul, GPU) e 2 (laranja, CPU) da paleta categórica de referência, validados
+(CVD ΔE 24,7; visão normal 33,6; contraste ≥ 3:1 sobre #fcfcfb). Uma série → sem legenda;
+duas séries → legenda + rótulos diretos. Um único eixo y por gráfico.
+
+Uso: python -m analysis.charts reports/pilot
+"""
+import json
+import os
+import sys
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+
+SURFACE, INK, INK_2, GRID = "#fcfcfb", "#0b0b0b", "#52514e", "#e6e5e1"
+GPU, CPU = "#2a78d6", "#eb6834"
+NAMES = {"semif": "SemIf", "rizzo_flow": "Rizzo Flow", "laya": "Laya", "gliner": "GLiNER"}
+P95_LIMIT_MS = 150
+
+plt.rcParams.update({
+    "svg.fonttype": "none", "font.family": "sans-serif", "font.size": 11,
+    "axes.edgecolor": GRID, "axes.labelcolor": INK_2, "xtick.color": INK_2, "ytick.color": INK_2,
+    "axes.facecolor": SURFACE, "figure.facecolor": SURFACE, "text.color": INK,
+})
+
+
+def save(fig, path: Path):
+    fig.savefig(path)
+    preview = os.environ.get("DMB_CHART_PREVIEW")  # PNG para inspeção visual, fora do repositório
+    if preview:
+        fig.savefig(Path(preview) / path.with_suffix(".png").name, dpi=110)
+
+
+def load(dest: Path) -> dict:
+    out = {}
+    for f in dest.glob("summary-*.json"):
+        s = json.loads(f.read_text())
+        out[(s["candidate"]["name"], s["device"])] = s
+    return out
+
+
+def style(ax, grid_axis="x"):
+    for side in ("top", "right"):
+        ax.spines[side].set_visible(False)
+    ax.grid(axis=grid_axis, color=GRID, linewidth=0.8)
+    ax.set_axisbelow(True)
+
+
+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])
+    fig, ax = plt.subplots(figsize=(7, 2.8))
+    bars = ax.barh([r[0] for r in rows], [r[1] for r in rows], height=0.55, color=GPU)
+    for bar, (_, v) in zip(bars, rows):
+        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)
+    style(ax)
+    fig.tight_layout()
+    save(fig, dest / "chart-acuracia.svg")
+    plt.close(fig)
+
+
+def latency_chart(data, dest):
+    cands = sorted({c for c, _ in data}, key=lambda c: data[(c, "cuda")]["latency_ms"]["p50"])
+    fig, ax = plt.subplots(figsize=(7, 3.0))
+    for i, c in enumerate(cands):
+        g = data[(c, "cuda")]["latency_ms"]["p50"]
+        cpu = data.get((c, "cpu"), {}).get("latency_ms", {}).get("p50")
+        if cpu:
+            ax.plot([g, cpu], [i, i], color=GRID, linewidth=2, zorder=1)
+            ax.scatter(cpu, i, s=70, color=CPU, edgecolor=SURFACE, linewidth=2, zorder=3,
+                       label="CPU (8 OCPU)" if i == 0 else None)
+            ax.text(cpu * 1.18, i, f"{cpu / 1000:.1f} s" if cpu >= 1000 else f"{cpu:.0f} ms",
+                    va="center", color=INK, fontsize=10)
+        ax.scatter(g, i, s=70, color=GPU, edgecolor=SURFACE, linewidth=2, zorder=3,
+                   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)
+    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)")
+    ax.set_title("Latência por decisão: GPU × CPU (lote 1)", loc="left", fontsize=12, color=INK)
+    ax.legend(loc="lower right", frameon=False, fontsize=10)
+    style(ax)
+    fig.tight_layout()
+    save(fig, dest / "chart-latencia.svg")
+    plt.close(fig)
+
+
+def tradeoff_chart(data, dest):
+    fig, ax = plt.subplots(figsize=(7, 3.6))
+    ax.axvline(P95_LIMIT_MS, color=INK_2, linewidth=1, linestyle=(0, (4, 3)))
+    ax.text(P95_LIMIT_MS + 3, 102, f"limite p95 = {P95_LIMIT_MS} ms", color=INK_2, fontsize=9, va="top")
+    for (c, d), s in data.items():
+        if d != "cuda":
+            continue
+        x, y = s["latency_ms"]["p95"], s["quality"]["accuracy"] * 100
+        ax.scatter(x, y, s=80, color=GPU, edgecolor=SURFACE, linewidth=2, zorder=3)
+        ax.annotate(NAMES[c], (x, y), xytext=(8, -4), textcoords="offset points", color=INK, fontsize=10)
+    ax.set_xlim(0, 220)
+    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)
+    style(ax, grid_axis="both")
+    fig.tight_layout()
+    save(fig, dest / "chart-qualidade-latencia.svg")
+    plt.close(fig)
+
+
+def main():
+    dest = Path(sys.argv[1])
+    data = load(dest)
+    accuracy_chart(data, dest)
+    latency_chart(data, dest)
+    tradeoff_chart(data, dest)
+    print(sorted(p.name for p in dest.glob("chart-*.svg")))
+
+
+if __name__ == "__main__":
+    main()

infra/ds/collect.py

@@ -67,14 +67,20 @@ def main():
     table = ("| 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 |\n"
              "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n" + "\n".join(rows))
-    readme = (f"# Resultados — partição `{args.split}`"
+    start, end = "<!-- TABELA:INICIO -->", "<!-- TABELA:FIM -->"
-              + (f", {args.examples} exemplos" if args.examples else "") + "\n\n"
+    block = f"{start}\n{table}\n{end}"
-              "Gerado por `infra/ds/collect.py` a partir dos buckets de resultados. CPU: us-chicago-1 "
+    readme_path = dest / "README.md"
-              "(`VM.Standard.E4.Flex`, 8 OCPU, sub-rede privada). GPU: sa-saopaulo-1 (`VM.GPU.A10.1`). "
+    if readme_path.exists() and start in readme_path.read_text():
-              "Lote 1; latências excluem os exemplos de aquecimento. Probabilidades ainda sem calibração.\n\n"
+        # README editado à mão: só a tabela entre os marcadores é regenerada.
-              + table + "\n")
+        text = readme_path.read_text()
-    (dest / "README.md").write_text(readme)
+        readme = text[:text.index(start)] + block + text[text.index(end) + len(end):]
-    print(readme)
+    else:
+        readme = (f"# Resultados — partição `{args.split}`"
+                  + (f", {args.examples} exemplos" if args.examples else "") + "\n\n"
+                  "Gerado por `infra/ds/collect.py`. Lote 1; latências sem os exemplos de aquecimento; "
+                  "probabilidades sem calibração.\n\n" + block + "\n")
+    readme_path.write_text(readme)
+    print(table)
 
 
 if __name__ == "__main__":

reports/pilot/README.md

@@ -1,7 +1,37 @@
-# Resultados — partição `dev`, 50 exemplos
+# Piloto — 50 exemplos de desenvolvimento por candidato
 
-Gerado por `infra/ds/collect.py` a partir dos buckets de resultados. CPU: us-chicago-1 (`VM.Standard.E4.Flex`, 8 OCPU, sub-rede privada). GPU: sa-saopaulo-1 (`VM.GPU.A10.1`). Lote 1; latências excluem os exemplos de aquecimento. Probabilidades ainda sem calibração.
+## Sumário executivo
 
+O piloto validou, de ponta a ponta, a execução dos quatro candidatos sobre a mesma amostra de 50 enunciados de atendimento de telefonia móvel, em CPU e em GPU. Não houve erros de execução nem truncamento de entrada, e nenhum parâmetro operacional precisou de ajuste.
+
+- **Qualidade:** os dois modelos de 4B parâmetros lideram. O **SemIf** acertou **92%** e o **Rizzo Flow** **82–86%**. Os encoders compactos ficaram abaixo: **Laya** com 66% e **GLiNER** com 48%.
+- **Abstenção:** o SemIf reconheceu 10 de 12 pedidos sem intenção correspondente; o Laya, 4 de 12. Esse é o comportamento que evita rotas erradas no orquestrador.
+- **Latência em GPU A10:** Laya e GLiNER respondem em cerca de **20 ms**; SemIf e Rizzo, em cerca de **110 ms** de mediana. Mas o **p95 dos modelos de 4B (175–181 ms) fica acima do limite pré-registrado de 150 ms**.
+- **CPU:** só Laya e GLiNER são viáveis (p95 de 0,2 a 0,5 s). SemIf e Rizzo levam de 13 a 31 s por decisão e ficam fora das rodadas em CPU.
+- **Leitura preliminar:** há um *trade-off* claro entre qualidade e latência, que a rodada completa de desenvolvimento, a calibração e o teste final (1.744 exemplos) vão quantificar com intervalos de confiança.
+
+## Resultados gerais
+
+![Acurácia por candidato](chart-acuracia.svg)
+
+![Qualidade × latência em GPU](chart-qualidade-latencia.svg)
+
+![Latência por decisão: GPU × CPU](chart-latencia.svg)
+
+| Candidato | Acertos dentro do escopo (38) | `sem_correspondencia` reconhecido (12) | Escopo marcado como `sem_correspondencia` |
+|---|---|---|---|
+| SemIf | 36 | 10 | 2 |
+| Rizzo Flow | 34 | 7 | 2 |
+| Laya | 29 | 4 | 0 |
+| GLiNER | 19 | 5 | 11 |
+
+*(GPU A10. Em CPU os números coincidem, exceto o Rizzo Flow, que acerta 2 casos a mais: os backends CPU e CUDA do llama.cpp não são numericamente idênticos.)*
+
+## Resultados detalhados
+
+Lote 1. As latências excluem os 5 primeiros exemplos (aquecimento). ECE calculado sobre as probabilidades brutas, ainda sem calibração.
+
+<!-- TABELA:INICIO -->
 | 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 | 50 | 48.0% | 43.9% | 0.198 | 244.0 | 481.0 | 511.6 | 21.0 | 3257 | — | 0 | 0 |
@@ -12,3 +42,33 @@ Gerado por `infra/ds/collect.py` a partir dos buckets de resultados. CPU: us-chi
 | rizzo_flow | cuda | gguf-q8_0 | 50 | 82.0% | 84.3% | 0.103 | 108.1 | 174.7 | 182.5 | 5.3 | 4527 | 5107 | 0 | 0 |
 | semif | cpu | bfloat16 | 50 | 92.0% | 93.9% | 0.079 | 30633.4 | 47585.7 | 49344.0 | 6.3 | 9137 | — | 0 | 0 |
 | semif | cuda | bfloat16 | 50 | 92.0% | 93.9% | 0.114 | 113.8 | 181.3 | 183.7 | 6.7 | 9020 | 8513 | 0 | 0 |
+<!-- TABELA:FIM -->
+
+Arquivos: `summary-<candidato>-<dispositivo>.json` (resumo da execução, com cenário, precisão e diagnósticos) e `predictions-<candidato>-<dispositivo>.jsonl` (uma linha por exemplo, com a distribuição de probabilidades completa).
+
+## Metodologia
+
+**Amostra.** São 50 exemplos sorteados com seed fixa (20261007) da partição de desenvolvimento do conjunto congelado `d1-v1`. Os candidatos recebem os mesmos exemplos, na mesma ordem.
+
+| Característica | Composição da amostra |
+|---|---|
+| Intenções distintas | 29 das 42 da taxonomia |
+| Rótulo `sem_correspondencia` | 12: 8 pedidos fora de escopo e 4 com a intenção correta fora das opções |
+| Opções por pergunta | 4 (15 exemplos) · 8 (14) · 12 (9) · 16 (12) |
+| Perfis de escrita | informal/abreviado (12), erros de digitação (11), curto/neutro (8), regionalismo (8), emocional (5), indireto/várias intenções (5), longo (1) |
+
+**Tarefa.** Classificação de intenção (D1): para cada enunciado, o candidato escolhe uma opção entre as intenções elegíveis do exemplo mais `sem_correspondencia`, pelo contrato `POST /v1/systemone` (`jev-compat-v1`).
+
+**Execução.** Lote 1 e sem paralelismo; cada candidato na sua precisão nativa (fp32 para Laya e GLiNER, bf16 para SemIf, GGUF Q8_0 para Rizzo Flow). Inferência isolada de fontes externas, com artefatos verificados por SHA-256.
+
+**Cenário.** CPU em us-chicago-1 (`VM.Standard.E4.Flex`, 8 OCPU, 64 GB). GPU em sa-saopaulo-1 (`VM.GPU.A10.1`). Mais detalhes em [docs/05](../../docs/05-cenario-de-execucao.md).
+
+**Limites desta etapa.** Com 50 exemplos, cada ponto percentual de acurácia corresponde a meio exemplo, e os intervalos de confiança são largos (cerca de ±13 p.p. a 95%). O piloto serve para validar a execução e dar a ordem de grandeza, não para ranquear.
+
+## Conclusão
+
+1. **Execução validada.** Os quatro candidatos rodam isolados, sem erros e sem truncamento, com a mesma entrada e o mesmo contrato, em CPU e em GPU. O piloto não exigiu ajuste operacional; o orçamento interno de opções do Laya foi verificado e mantido no padrão.
+2. **CPU restrita aos encoders.** Pela regra pré-registrada (p95 ≤ 10 s), SemIf e Rizzo Flow ficam fora das rodadas em CPU.
+3. **Sinal de qualidade.** Os modelos de 4B lideram em acurácia e em abstenção correta. Entre os compactos, o Laya acerta mais dentro do escopo, mas quase não abstém; o GLiNER marca muitos pedidos válidos como `sem_correspondencia`.
+4. **Sinal de latência.** Em GPU, só Laya e GLiNER cumprem o p95 ≤ 150 ms. SemIf e Rizzo ficam perto do limite (cerca de 110 ms de mediana), mas acima dele no p95.
+5. **Próximos passos.** Rodada completa de desenvolvimento (1.183 exemplos), calibração das probabilidades e escolha de limiares de abstenção (1.127 exemplos), congelamento da configuração e teste final (1.744 exemplos), com intervalos de confiança e cortes por perfil e número de opções.

reports/pilot/chart-acuracia.svg

@@ -0,0 +1,251 @@
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+    <dc:date>2026-10-07T21:19:36.948395</dc:date>
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