File size: 12,729 Bytes
ed1b365 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | """
Dashboard - ASCII-formatted system status display for the Codette training lab.
Shows:
- Latest training run stats
- Best adapter scores
- Dataset sizes and quality
- Failure rates
- Improvement trends
No web framework required; pure terminal output.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional
_THIS_DIR = Path(__file__).resolve().parent
_PROJECT_ROOT = _THIS_DIR.parent
if str(_PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(_PROJECT_ROOT))
from observatory.metrics_logger import MetricsLogger
from observatory.performance_tracker import PerformanceTracker
from observatory.dataset_quality_monitor import DatasetQualityMonitor
class Dashboard:
"""ASCII dashboard for the Codette training lab."""
WIDTH = 76
def __init__(
self,
metrics_log: Optional[str] = None,
quality_log: Optional[str] = None,
eval_results: Optional[str] = None,
):
self.logger = MetricsLogger(log_file=metrics_log)
self.tracker = PerformanceTracker(logger=self.logger)
self.quality_monitor = DatasetQualityMonitor(quality_file=quality_log)
self.eval_results_path = eval_results
# -- sections ----------------------------------------------------------
def _header(self) -> List[str]:
lines = []
lines.append("")
lines.append("+" + "=" * (self.WIDTH - 2) + "+")
lines.append("|" + " CODETTE TRAINING LAB OBSERVATORY ".center(self.WIDTH - 2) + "|")
lines.append("|" + f" {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S UTC')} ".center(self.WIDTH - 2) + "|")
lines.append("+" + "=" * (self.WIDTH - 2) + "+")
return lines
def _section(self, title: str) -> List[str]:
lines = []
lines.append("")
lines.append("+" + "-" * (self.WIDTH - 2) + "+")
lines.append("|" + f" {title} ".ljust(self.WIDTH - 2) + "|")
lines.append("+" + "-" * (self.WIDTH - 2) + "+")
return lines
def _row(self, label: str, value: str) -> str:
"""Single label: value row."""
content = f" {label:<30s} {value}"
return "|" + content.ljust(self.WIDTH - 2) + "|"
def _bar_row(self, label: str, value: float, max_width: int = 30) -> str:
"""Row with ASCII progress bar."""
filled = int(value * max_width)
bar = "[" + "#" * filled + "." * (max_width - filled) + "]"
content = f" {label:<22s} {value:>6.3f} {bar}"
return "|" + content.ljust(self.WIDTH - 2) + "|"
def _empty_row(self) -> str:
return "|" + " " * (self.WIDTH - 2) + "|"
def _footer(self) -> List[str]:
return ["+" + "=" * (self.WIDTH - 2) + "+", ""]
# -- sections ----------------------------------------------------------
def _latest_training_section(self) -> List[str]:
lines = self._section("LATEST TRAINING RUN")
latest = self.logger.get_latest()
if not latest:
lines.append(self._row("Status", "No training runs logged yet"))
return lines
lines.append(self._row("Adapter", latest.get("adapter", "N/A")))
lines.append(self._row("Timestamp", latest.get("timestamp", "N/A")))
lines.append(self._row("Dataset Version", latest.get("dataset_version", "N/A")))
lines.append(self._row("Dataset Size", str(latest.get("dataset_size", 0))))
lines.append(self._row("Epoch", str(latest.get("epoch", 0))))
lines.append(self._bar_row("Reasoning Score", latest.get("reasoning_score", 0)))
lines.append(self._row("Loss", f"{latest.get('loss', 0):.6f}"))
params = latest.get("training_params", {})
if params:
lines.append(self._empty_row())
lines.append(self._row("Training Parameters", ""))
for k, v in list(params.items())[:6]:
lines.append(self._row(f" {k}", str(v)))
return lines
def _best_adapters_section(self) -> List[str]:
lines = self._section("TOP ADAPTERS")
best = self.tracker.best_adapters(top_n=5)
if not best:
lines.append(self._row("Status", "No adapter data available"))
return lines
# Table header
hdr = f" {'#':<3} {'Adapter':<26} {'Score':>7} {'Loss':>7} {'Epoch':>5}"
lines.append("|" + hdr.ljust(self.WIDTH - 2) + "|")
sep = f" {'--':<3} {'------':<26} {'-----':>7} {'----':>7} {'-----':>5}"
lines.append("|" + sep.ljust(self.WIDTH - 2) + "|")
for i, entry in enumerate(best, 1):
name = entry.get("adapter", "?")[:25]
score = entry.get("reasoning_score", 0)
loss = entry.get("loss", 0)
epoch = entry.get("epoch", 0)
row = f" {i:<3} {name:<26} {score:>7.4f} {loss:>7.4f} {epoch:>5}"
lines.append("|" + row.ljust(self.WIDTH - 2) + "|")
return lines
def _dataset_quality_section(self) -> List[str]:
lines = self._section("DATASET QUALITY")
latest = self.quality_monitor.get_latest()
if not latest:
lines.append(self._row("Status", "No quality data recorded"))
return lines
lines.append(self._row("Dataset Version", latest.get("dataset_version", "N/A")))
lines.append(self._row("Total Examples", str(latest.get("total_examples", 0))))
lines.append(self._row("Valid Examples", str(latest.get("valid_examples", 0))))
lines.append(self._bar_row("Validity Rate", latest.get("validity_rate", 0)))
lines.append(self._row("Avg Response Length", f"{latest.get('avg_response_length', 0):.1f} words"))
lines.append(self._row("Duplicate Rate", f"{latest.get('duplicate_rate', 0):.2%}"))
lines.append(self._row("Near-Duplicate Rate", f"{latest.get('near_duplicate_rate', 0):.2%}"))
lines.append(self._bar_row("Topic Diversity", min(latest.get("topic_diversity", 0) * 10, 1.0)))
lines.append(self._row("Topic Concentration", f"{latest.get('topic_concentration', 0):.2%}"))
# Regressions
regressions = self.quality_monitor.check_latest_regressions()
if regressions:
lines.append(self._empty_row())
for r in regressions:
sev = r["severity"].upper()
msg = f" [{sev}] {r['metric']}: {r['percent_change']:+.1f}%"
lines.append("|" + msg.ljust(self.WIDTH - 2) + "|")
return lines
def _improvement_trends_section(self) -> List[str]:
lines = self._section("IMPROVEMENT TRENDS")
trends = self.tracker.improvement_trends()
if not trends:
lines.append(self._row("Status", "Insufficient data for trends"))
return lines
for t in trends[:5]:
name = t["adapter"][:22]
delta = t["delta"]
pct = t["percent_change"]
runs = t["num_runs"]
sign = "+" if delta >= 0 else ""
indicator = "^" if delta > 0.01 else ("v" if delta < -0.01 else "=")
row = (f" {indicator} {name:<22} "
f"delta: {sign}{delta:.4f} "
f"({sign}{pct:.1f}%) "
f"[{runs} runs]")
lines.append("|" + row.ljust(self.WIDTH - 2) + "|")
return lines
def _failure_rates_section(self) -> List[str]:
lines = self._section("EVALUATION FAILURE RATES")
if not self.eval_results_path or not os.path.exists(self.eval_results_path):
lines.append(self._row("Status", "No evaluation results file specified"))
return lines
try:
with open(self.eval_results_path, "r", encoding="utf-8") as f:
results = json.load(f)
except (json.JSONDecodeError, OSError):
lines.append(self._row("Status", "Could not load evaluation results"))
return lines
# Overall score
overall = results.get("overall", {})
if overall:
overall_score = overall.get("overall", 0)
lines.append(self._bar_row("Overall Score", overall_score))
lines.append(self._empty_row())
# Per-category scores
categories = results.get("categories", {})
if categories:
hdr = f" {'Category':<20} {'Score':>7} {'Prompts':>8}"
lines.append("|" + hdr.ljust(self.WIDTH - 2) + "|")
sep = f" {'--------':<20} {'-----':>7} {'-------':>8}"
lines.append("|" + sep.ljust(self.WIDTH - 2) + "|")
for cat, data in sorted(categories.items()):
avg = data.get("average_scores", {}).get("overall", 0)
n = data.get("prompts_scored", 0)
status = "*" if avg < 0.4 else ("~" if avg < 0.55 else " ")
row = f" {status}{cat:<19} {avg:>7.4f} {n:>8}"
lines.append("|" + row.ljust(self.WIDTH - 2) + "|")
lines.append(self._empty_row())
lines.append("|" + " * = failing, ~ = weak".ljust(self.WIDTH - 2) + "|")
return lines
def _sparkline_section(self) -> List[str]:
lines = self._section("SCORE HISTORY")
adapters = self.logger.get_unique_adapters()
if not adapters:
lines.append(self._row("Status", "No history data"))
return lines
for adapter in adapters[:6]:
progression = self.tracker.score_progression(adapter)
if not progression:
continue
scores = [p["reasoning_score"] for p in progression]
spark = PerformanceTracker._sparkline(scores, width=30)
name = adapter[:20]
row = f" {name:<21} {spark} [{scores[0]:.3f}->{scores[-1]:.3f}]"
lines.append("|" + row.ljust(self.WIDTH - 2) + "|")
return lines
# -- main render -------------------------------------------------------
def render(self) -> str:
"""Render the complete dashboard."""
all_lines: List[str] = []
all_lines.extend(self._header())
all_lines.extend(self._latest_training_section())
all_lines.extend(self._best_adapters_section())
all_lines.extend(self._dataset_quality_section())
all_lines.extend(self._improvement_trends_section())
all_lines.extend(self._failure_rates_section())
all_lines.extend(self._sparkline_section())
all_lines.extend(self._footer())
return "\n".join(all_lines)
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def main() -> None:
parser = argparse.ArgumentParser(
description="Codette Observatory Dashboard - ASCII system status display"
)
parser.add_argument(
"--metrics-log", "-m",
default=None,
help="Path to observatory_metrics.json",
)
parser.add_argument(
"--quality-log", "-q",
default=None,
help="Path to dataset_quality_log.json",
)
parser.add_argument(
"--eval-results", "-e",
default=None,
help="Path to benchmark evaluation results JSON",
)
parser.add_argument(
"--section", "-s",
choices=["training", "adapters", "quality", "trends", "failures", "history", "all"],
default="all",
help="Show only a specific section (default: all)",
)
args = parser.parse_args()
dashboard = Dashboard(
metrics_log=args.metrics_log,
quality_log=args.quality_log,
eval_results=args.eval_results,
)
if args.section == "all":
print(dashboard.render())
else:
section_map = {
"training": dashboard._latest_training_section,
"adapters": dashboard._best_adapters_section,
"quality": dashboard._dataset_quality_section,
"trends": dashboard._improvement_trends_section,
"failures": dashboard._failure_rates_section,
"history": dashboard._sparkline_section,
}
func = section_map.get(args.section)
if func:
lines = dashboard._header()
lines.extend(func())
lines.extend(dashboard._footer())
print("\n".join(lines))
if __name__ == "__main__":
main()
|