Unified, nicer result tables + conda env setup in README

- cli: rich Run Summary table for multi-benchmark runs (green/red rows,
  fallback to aligned plain text); unified _fmt_score (fractions render
  as percentages everywhere -- was 1.0 in summary vs 100.0% in detail);
  fix the stray "summary csv -> None/viz/..." print without --out-dir;
  summary.md upgraded to a proper table with model/timestamp/ok-count
  header -- one table for a whole N-benchmark run
- text renderer: single-bench headline deduped (dataset==recipe) and
  compacted to one facts line; adaptive metric-name column (long names
  no longer break alignment)
- md_compare: auto-switches to one-row-per-benchmark when comparing
  different benchmarks with different metrics; same-bench model
  comparison gains baseline delta markers (+/- percentage points)
- README: conda create/activate in the install block

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

README.md

@@ -28,24 +28,17 @@
 
 ## 安装
 
-要求 Python ≥ 3.10。
+要求 Python ≥ 3.10，一条命令装完即可跑全部 28 个 benchmark，无任何可选依赖
+（代码执行类需要宿主机有 Docker，镜像判分时自动拉取）。
 
 ```bash
 git clone https://git.meta-stone.net/sora/EvalHarness.git
 cd EvalHarness
+conda create -n evalharness python=3.10 -y
+conda activate evalharness
 pip install .
 ```
 
-**一条命令装完即可跑全部 28 个 benchmark，没有任何可选依赖。**
-
-| 官方判分逻辑形态 | 涉及 benchmark | 位置 |
-|---|---|---|
-| 纯 Python 算法 | 数学、DROP、MCQ | 核心依赖（sympy/numpy/scipy） |
-| BFCL 官方 AST 判定器 | bfcl_v3 | 已内置（`evalharness/third_party/bfcl/`，Apache-2.0） |
-| 官方执行环境 | humaneval、bigcodebench、live_code_bench、swe_bench | Docker 镜像，判分时按需拉取 |
-
-另有本地引擎类（`tau2_bench`）使用官方 tau2 包（本地源码安装，无重依赖）。
-
 离线验证安装（不需要模型、不联网）：
 
 ```bash
@@ -54,7 +47,6 @@ evalharness eval run gsm8k --model mock:boxed --limit 8
 # 数据 → prompt → 生成 → 判分 → 报告 全链路可用
 ```
 
-代码执行类 benchmark 需要宿主机有 Docker。
 
 ## 快速开始
 

evalharness/cli.py

@@ -332,14 +332,7 @@ def _cmd_eval_run(args) -> int:
                                     'failed', _time.time() - t0)
 
     if len(rows) > 1:
-        print(f'\n{"benchmark":<20} {"metric":<14} {"score":>8} {"n":>5} {"time":>9}')
+        _print_summary_table(console, rows)
-        print('-' * 62)
-        for r in rows:
-            val = 'ERR' if not r['ok'] else _f3(r['value'])
-            h = r.get('hours') or 0
-            tdisp = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
-            print(f"{r['name']:<20} {r['metric']:<14} {val!s:>8} {r.get('n', '')!s:>5} {tdisp:>9}"
-                  + (f"  {r.get('err', '')}" if not r['ok'] else ''))
         ok = sum(1 for r in rows if r['ok'])
         print(f'\n{ok}/{len(rows)} ok' + (f' -> artifacts in {out_dir}/' if out_dir else ''))
         if out_dir:
@@ -371,13 +364,65 @@ def _cmd_eval_run(args) -> int:
                                    'tpot_mean_s', 'tpot_p90_s', 'tpot_p99_s')] +
                                [cats])
             with open(f'{out_dir}/viz/summary.md', 'w', encoding='utf-8') as f:
-                f.write(f'# eval run summary\n\n| dataset | metric | value | secs |\n|---|---|---|---|\n')
+                import time as _tt
+                model_names = {r.get('model', '') for r in rows if r.get('model')}
+                head = f"# eval run summary\n\n- model: {', '.join(model_names) or '?'}\n"
+                head += f"- created: {_tt.strftime('%Y-%m-%d %H:%M:%S')}\n"
+                head += f"- benchmarks: {sum(1 for r in rows if r['ok'])}/{len(rows)} ok\n\n"
+                f.write(head)
+                f.write('| benchmark | metric | score | n | time | status |\n'
+                        '|---|---|---:|---:|---:|---|\n')
                 for r in rows:
-                    f.write(f"| {r['name']} | {r['metric']} | {r['value']} | {r['secs']} |\n")
+                    v = _fmt_score(r.get('value'))
-        print(f'summary csv -> {out_dir}/viz/summary.csv')
+                    h = r.get('hours') or 0
+                    t = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
+                    st = 'ok' if r['ok'] else f"failed: {r.get('err', '')[:60]}"
+                    f.write(f"| {r['name']} | {r['metric']} | {v} | "
+                            f"{r.get('n', '')} | {t} | {st} |\n")
+        if out_dir:
+            print(f'summary     -> {out_dir}/viz/summary.md (+ summary.csv)')
     return 0 if all(r['ok'] for r in rows) else 1
 
 
+def _fmt_score(v):
+    """Unified score format: fractions render as percentages everywhere."""
+    try:
+        v = float(v)
+    except (TypeError, ValueError):
+        return 'ERR'
+    return f'{v * 100:.1f}%' if 0.0 <= v <= 1.0 else f'{v:g}'
+
+
+def _print_summary_table(console, rows):
+    """Rich multi-benchmark summary (falls back to aligned plain text)."""
+    if console is not None:
+        from rich.table import Table
+
+        t = Table(title='Run Summary', header_style='bold cyan',
+                  title_style='bold', expand=False)
+        for col, just in (('benchmark', 'left'), ('metric', 'left'),
+                          ('score', 'right'), ('n', 'right'), ('time', 'right')):
+            t.add_column(col, justify=just)
+        for r in rows:
+            v = _fmt_score(r.get('value')) if r['ok'] else 'ERR'
+            h = r.get('hours') or 0
+            tm = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
+            style = 'green' if r['ok'] else 'red'
+            t.add_row(r['name'], r['metric'], v, str(r.get('n', '')), tm,
+                      style=style)
+        console.print(t)
+        return
+    print(f'\n{"benchmark":<20} {"metric":<16} {"score":>8} {"n":>6} {"time":>8}')
+    print('-' * 64)
+    for r in rows:
+        v = _fmt_score(r.get('value')) if r['ok'] else 'ERR'
+        h = r.get('hours') or 0
+        tm = f'{h:.2f}h' if h >= 0.995 else f"{r.get('secs', 0):.0f}s"
+        err = f"  {r.get('err', '')}" if not r['ok'] else ''
+        print(f"{r['name']:<20} {r['metric']:<16} {v:>8} "
+              f"{str(r.get('n', '')):>6} {tm:>8}{err}")
+
+
 def _f3(v):
     try:
         return round(float(v), 4)

evalharness/viz/renderers/text.py

@@ -21,37 +21,42 @@ def text_table(target: Union[EvalReport, List[EvalReport]], opts: Dict) -> str:
     reports = target if isinstance(target, list) else [target]
     out: List[str] = []
     for rep in reports:
-        head = f'{rep.dataset}  [{rep.recipe}]  model={rep.model or "?"}  n={rep.num_samples}'
+        # headline: dedupe dataset/recipe when identical; join facts compactly
-        out.append('=' * max(len(head), 40))
+        title = rep.dataset if rep.dataset == rep.recipe else f'{rep.dataset} [{rep.recipe}]'
+        facts = [f'model={rep.model or "?"}', f'n={rep.num_samples}']
+        info = rep.metric_groups.get('run_info', {}) or {}
+        secs = sum(float((s.usage or {}).get('latency_s', 0) or 0) for s in rep.samples)
+        if secs >= 3600:
+            facts.append(f'time={secs / 3600:.2f}h')
+        elif secs:
+            facts.append(f'time={secs:.0f}s')
+        if info.get('gen_total_tokens'):
+            facts.append(f'tokens={info["gen_total_tokens"]}')
+        head = f'{title}  ·  ' + '  '.join(facts)
         out.append(head)
         out.append('=' * max(len(head), 40))
-        # run stats: duration, tokens, cost (from run_info + usage aggregates)
+
-        info = rep.metric_groups.get('run_info', {}) or {}
-        total_tokens = info.get('gen_total_tokens', 0)
-        tok_s = ''
-        if total_tokens:
-            tok_s = f'  tokens={total_tokens}'
-        secs = 0.0
-        for s in rep.samples:
-            secs += float((s.usage or {}).get('latency_s', 0) or 0)
-        dur = f'  time={secs / 3600:.2f}h' if secs >= 3600 else (f'  time={secs:.0f}s' if secs else '')
-        if dur or tok_s:
-            out.append(f'n_samples={rep.num_samples}{dur}{tok_s}')
         if rep.num_failed_extractions:
             warn = (f'!! {rep.num_failed_extractions}/{rep.num_samples} extractions failed '
                     f'({_pct(rep.metrics.get("extraction_failure_rate", 0))}) -- check recipe/model fit')
             out.append(warn)
-        for metric, value in rep.metrics.items():
+
-            if metric == 'extraction_failure_rate':
+        metrics = [(m, v) for m, v in rep.metrics.items()
-                continue
+                   if m != 'extraction_failure_rate' and isinstance(v, (int, float))]
-            out.append(f'{metric:<16} {_pct(value):>7}  {_bars(value)}')
+        w = max([len(m) for m, _ in metrics] + [12])   # adaptive, long names survive
+        for metric, value in metrics:
+            out.append(f'{metric:<{w}}  {_pct(value):>8}  {_bars(value)}')
+
         for group_name, groups in rep.metric_groups.items():
             if group_name == 'run_info' or group_name.startswith('agg_error'):
                 continue
+            numeric = {g: v for g, v in groups.items() if isinstance(v, (int, float))}
+            if not numeric:
+                continue
+            gw = max([len(str(g)) for g in numeric] + [12])
             out.append(f'-- {group_name} ' + '-' * max(0, 30 - len(group_name)))
-            for g, v in groups.items():
+            for g, v in numeric.items():
-                if isinstance(v, (int, float)):
+                out.append(f'   {str(g):<{gw}}  {_pct(v):>8}  {_bars(v, 20)}')
-                    out.append(f'   {g:<28} {_pct(v):>7}  {_bars(v, 20)}')
         out.append('')
     return '\n'.join(out)
 
@@ -79,22 +84,58 @@ def markdown(target: Union[EvalReport, List[EvalReport]], opts: Dict) -> str:
 
 @register_renderer('md_compare')
 def md_compare(target: List[EvalReport], opts: Dict) -> str:
-    """Side-by-side metric table for N reports (e.g. two models on one bench)."""
+    """Side-by-side metric table for N reports (models on one bench, or many
+    benches of one model -- the shape is the same: one row per benchmark).
+    The first report is the baseline; later columns get a delta marker."""
     if not isinstance(target, list) or len(target) < 1:
         raise ValueError('md_compare needs a list of reports')
+
+    # many DIFFERENT benchmarks, each with its own primary metric: a compact
+    # one-row-per-bench table beats a sparse metric-x-bench grid
+    datasets = {r.dataset for r in target}
+    primary_metrics = {next((m for m in r.metrics if m != 'extraction_failure_rate'), '')
+                       for r in target}
+    if len(datasets) > 1 and len(target) == len(datasets) and len(primary_metrics) > 1:
+        out = ['# Comparison', '', '| benchmark | metric | score | n |', '|---|---|---:|---:|']
+        best = max((next((v for m, v in r.metrics.items()
+                          if m != 'extraction_failure_rate'), 0.0) for r in target))
+        for r in target:
+            m = next((m for m in r.metrics if m != 'extraction_failure_rate'), '')
+            v = r.metrics.get(m, 0.0)
+            cell = f'**{_pct(v)}**' if v == best and len(target) > 1 else _pct(v)
+            out.append(f'| {r.dataset} | {m} | {cell} | {r.num_samples} |')
+        return '\n'.join(out + [''])
+
     metrics: List[str] = []
     for rep in target:
         for m in rep.metrics:
             if m not in metrics and m != 'extraction_failure_rate':
                 metrics.append(m)
-    cols = [f'{rep.dataset}/{rep.recipe}[{rep.model or "?"}]' for rep in target]
+    cols = []
-    out = ['# Comparison', '', '| metric | ' + ' | '.join(cols) + ' |',
+    for rep in target:
+        # models on one bench -> show the model; many benches -> show the bench
+        same_dataset = len({r.dataset for r in target}) == 1
+        name = rep.model or '?' if same_dataset else rep.dataset
+        cols.append(name)
+    out = ['# Comparison', '',
+           '| metric | ' + ' | '.join(cols) + ' |',
            '|---' * (len(cols) + 1) + '|']
     for m in metrics:
-        cells = [_pct(rep.metrics.get(m, 0.0)) for rep in target]
+        cells = []
-        best = max(rep.metrics.get(m, 0.0) for rep in target)
+        base = target[0].metrics.get(m, 0.0)
-        cells = [f'**{c}**' if rep.metrics.get(m, 0.0) == best and len(target) > 1 else c
+        for rep, col_i in zip(target, range(len(target))):
-                 for c, rep in zip(cells, target)]
+            v = rep.metrics.get(m, 0.0)
+            cell = _pct(v)
+            best = max(r.metrics.get(m, 0.0) for r in target)
+            if v == best and len(target) > 1:
+                cell = f'**{cell}**'
+            if rep is not target[0] and isinstance(base, (int, float)):
+                d = v - base
+                if d > 0.0005:
+                    cell += f' ▲{d * 100:+.1f}'
+                elif d < -0.0005:
+                    cell += f' ▼{d * 100:+.1f}'
+            cells.append(cell)
         out.append(f'| {m} | ' + ' | '.join(cells) + ' |')
     out.append('')
     return '\n'.join(out)