Hugging Face's logo

Spaces:
abr-6024
/
Negoti_Arena
Sleeping

App Files Community
Negoti_Arena
File size: 6,374 Bytes
8e52a2e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
"""

training/plot_curves.py — Generate reward curve HTML from training results

==========================================================================

Reads checkpoints/cpu_run/reward_curve.csv and produces a self-contained

HTML file with interactive Plotly charts — no matplotlib, no GPU.



Run:

    python -m training.plot_curves --input checkpoints/cpu_run/reward_curve.csv

    # Opens reward_curves.html in browser

"""

from __future__ import annotations

import argparse
import csv
import json
import os
import webbrowser


def load_csv(path: str) -> dict[str, list]:
    data: dict[str, list] = {}
    with open(path) as f:
        reader = csv.DictReader(f)
        for row in reader:
            for k, v in row.items():
                data.setdefault(k, []).append(float(v))
    return data


def generate_html(data: dict[str, list], output_path: str) -> str:
    steps = data.get("step", [])
    f1 = data.get("overseer_f1", [])
    det = data.get("detection_rate", [])
    fp  = data.get("false_positive_rate", [])

    # Compute improvement annotations
    f1_before = f1[0] if f1 else 0
    f1_after  = f1[-1] if f1 else 0
    gap = f1_after - f1_before

    html = f"""<!DOCTYPE html>

<html>

<head>

  <meta charset="UTF-8">

  <title>NegotiArena Training Curves</title>

  <script src="https://cdn.plot.ly/plotly-2.26.0.min.js"></script>

  <style>

    body {{ background:#0f0f1a; color:#e0e0e0; font-family:sans-serif;

           max-width:1100px; margin:40px auto; padding:20px; }}

    h1 {{ color:#4a9eff; }}

    h3 {{ color:#aaa; font-weight:400; }}

    .metric-row {{ display:flex; gap:20px; margin:24px 0; }}

    .card {{ background:#1a1a2e; border:1px solid #333; border-radius:8px;

             padding:20px; flex:1; text-align:center; }}

    .card .val {{ font-size:2.2em; font-weight:bold; margin:8px 0; }}

    .card .label {{ color:#aaa; font-size:0.9em; }}

    .green {{ color:#00cc66; }}

    .red   {{ color:#ff4a4a; }}

    .blue  {{ color:#4a9eff; }}

    footer {{ color:#555; font-size:0.8em; margin-top:40px; text-align:center; }}

  </style>

</head>

<body>

  <h1>🏛️ NegotiArena — Training Results</h1>

  <h3>REINFORCE on CPU → same reward signal used for GRPO on GPU</h3>



  <div class="metric-row">

    <div class="card">

      <div class="label">Overseer F1 (Before)</div>

      <div class="val red">{f1_before:.3f}</div>

      <div class="label">Random baseline</div>

    </div>

    <div class="card">

      <div class="label">Overseer F1 (After)</div>

      <div class="val green">{f1_after:.3f}</div>

      <div class="label">After {int(steps[-1]) if steps else 0} training steps</div>

    </div>

    <div class="card">

      <div class="label">Improvement</div>

      <div class="val blue">+{gap:.3f}</div>

      <div class="label">{gap/f1_before*100:.0f}% relative gain</div>

    </div>

    <div class="card">

      <div class="label">FP Rate (After)</div>

      <div class="val green">{fp[-1]:.1%}</div>

      <div class="label">Was {fp[0]:.1%}</div>

    </div>

  </div>



  <div id="chart_f1"></div>

  <div id="chart_det"></div>



  <script>

  const steps = {json.dumps(steps)};

  const f1    = {json.dumps(f1)};

  const det   = {json.dumps(det)};

  const fp    = {json.dumps(fp)};



  const layout = {{

    paper_bgcolor: '#0f0f1a',

    plot_bgcolor:  '#1a1a2e',

    font: {{ color: '#cccccc' }},

    xaxis: {{ title: 'Training Step', gridcolor: '#333' }},

    legend: {{ bgcolor: '#1a1a2e' }},

    height: 360,

    margin: {{ t: 50, b: 50 }},

  }};



  // Chart 1: F1 curve with before/after annotation

  Plotly.newPlot('chart_f1', [

    {{

      x: steps, y: f1, mode: 'lines+markers',

      name: 'Overseer F1', line: {{ color: '#00cc66', width: 3 }},

      marker: {{ size: 6 }}

    }},

    {{

      x: [steps[0]], y: [f1[0]], mode: 'markers',

      name: 'Before', marker: {{ color: '#ff4a4a', size: 14, symbol: 'circle' }}

    }},

    {{

      x: [steps[steps.length-1]], y: [f1[f1.length-1]], mode: 'markers',

      name: 'After', marker: {{ color: '#00cc66', size: 14, symbol: 'star' }}

    }}

  ], {{

    ...layout,

    title: {{ text: 'Overseer F1 — Coalition Detection Improvement', font: {{ color: '#4a9eff', size: 16 }} }},

    yaxis: {{ title: 'F1 Score', range: [0, 1], gridcolor: '#333' }},

    annotations: [{{

      x: steps[steps.length-1], y: f1[f1.length-1],

      text: ` After: ${{f1[f1.length-1].toFixed(3)}}`,

      showarrow: true, arrowhead: 2,

      font: {{ color: '#00cc66', size: 13 }},

      bgcolor: '#1a2e1a', bordercolor: '#00cc66',

    }}]

  }});



  // Chart 2: Detection rate vs FP rate

  Plotly.newPlot('chart_det', [

    {{

      x: steps, y: det, mode: 'lines', name: 'Detection Rate',

      line: {{ color: '#4a9eff', width: 2 }}

    }},

    {{

      x: steps, y: fp, mode: 'lines', name: 'False Positive Rate',

      line: {{ color: '#ff9a4a', width: 2, dash: 'dot' }}

    }}

  ], {{

    ...layout,

    title: {{ text: 'Detection Rate vs False Positive Rate', font: {{ color: '#4a9eff', size: 16 }} }},

    yaxis: {{ title: 'Rate', range: [0, 1], gridcolor: '#333' }},

  }});

  </script>



  <footer>

    NegotiArena | Meta × Scaler OpenEnv Hackathon | Theme 1: Multi-Agent Interactions<br>

    CPU training uses REINFORCE with the same reward signal as GPU GRPO training.

  </footer>

</body>

</html>"""

    with open(output_path, "w", encoding="utf-8") as f:
        f.write(html)

    return output_path


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input", default="checkpoints/cpu_run/reward_curve.csv")
    parser.add_argument("--output", default="reward_curves.html")
    parser.add_argument("--open", action="store_true", default=True,
                        help="Open in browser after generating")
    args = parser.parse_args()

    if not os.path.exists(args.input):
        print(f"❌ File not found: {args.input}")
        print("   Run training first: python -m training.train_cpu --steps 200")
        return

    data = load_csv(args.input)
    out = generate_html(data, args.output)
    print(f"✅ Chart saved to {out}")

    if args.open:
        webbrowser.open(f"file://{os.path.abspath(out)}")


if __name__ == "__main__":
    main()