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Dataset Quality Monitor - tracks dataset quality metrics across versions,
compares quality between iterations, and flags regressions.
"""
from __future__ import annotations
import json
import os
import sys
import threading
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))
_DEFAULT_QUALITY_FILE = Path(__file__).resolve().parent.parent / "dataset_quality_log.json"
class DatasetQualityMonitor:
"""Monitor dataset quality metrics across versions."""
# Thresholds for regression detection
REGRESSION_THRESHOLDS = {
"total_examples": -0.10, # >10% decrease in size
"avg_response_length": -0.15, # >15% decrease in avg length
"duplicate_rate": 0.05, # >5% absolute increase in duplicates
"topic_diversity": -0.10, # >10% decrease in diversity
}
def __init__(self, quality_file: Optional[str] = None):
self.quality_file = Path(quality_file) if quality_file else _DEFAULT_QUALITY_FILE
self._lock = threading.Lock()
self._ensure_file()
def _ensure_file(self) -> None:
if not self.quality_file.exists():
os.makedirs(self.quality_file.parent, exist_ok=True)
with open(self.quality_file, "w", encoding="utf-8") as f:
json.dump([], f)
def _read_all(self) -> List[Dict[str, Any]]:
with open(self.quality_file, "r", encoding="utf-8") as f:
try:
data = json.load(f)
except json.JSONDecodeError:
data = []
return data if isinstance(data, list) else []
def _write_all(self, entries: List[Dict[str, Any]]) -> None:
with open(self.quality_file, "w", encoding="utf-8") as f:
json.dump(entries, f, indent=2, default=str)
# -- recording ---------------------------------------------------------
def record_quality(
self,
dataset_version: str,
total_examples: int,
valid_examples: int,
avg_response_length: float,
duplicate_rate: float,
near_duplicate_rate: float,
topic_diversity: float,
topic_concentration: float,
min_length: int = 0,
max_length: int = 0,
too_short: int = 0,
too_long: int = 0,
extra: Optional[Dict[str, Any]] = None,
) -> Dict[str, Any]:
"""Record quality metrics for a dataset version.
Returns the recorded entry.
"""
entry: Dict[str, Any] = {
"timestamp": datetime.utcnow().isoformat() + "Z",
"dataset_version": dataset_version,
"total_examples": total_examples,
"valid_examples": valid_examples,
"invalid_examples": total_examples - valid_examples,
"validity_rate": round(valid_examples / max(total_examples, 1), 4),
"avg_response_length": round(avg_response_length, 1),
"duplicate_rate": round(duplicate_rate, 4),
"near_duplicate_rate": round(near_duplicate_rate, 4),
"topic_diversity": round(topic_diversity, 4),
"topic_concentration": round(topic_concentration, 4),
"min_length": min_length,
"max_length": max_length,
"too_short": too_short,
"too_long": too_long,
}
if extra:
entry["extra"] = extra
with self._lock:
entries = self._read_all()
entries.append(entry)
self._write_all(entries)
return entry
def record_from_validation_report(
self,
dataset_version: str,
report: Dict[str, Any],
) -> Dict[str, Any]:
"""Record quality from a DatasetValidator report dict."""
ls = report.get("response_length_stats", {})
total = report.get("total_lines", 0)
valid = report.get("valid", 0)
exact_dup = report.get("exact_duplicates", 0)
near_dup = report.get("near_duplicates", 0)
return self.record_quality(
dataset_version=dataset_version,
total_examples=total,
valid_examples=valid,
avg_response_length=ls.get("mean", 0),
duplicate_rate=exact_dup / max(total, 1),
near_duplicate_rate=near_dup / max(total, 1),
topic_diversity=report.get("unique_topics", 0) / max(total, 1),
topic_concentration=report.get("topic_concentration", 0),
min_length=ls.get("min", 0),
max_length=ls.get("max", 0),
too_short=report.get("too_short", 0),
too_long=report.get("too_long", 0),
)
# -- querying ----------------------------------------------------------
def get_all(self) -> List[Dict[str, Any]]:
"""Get all quality records."""
with self._lock:
return self._read_all()
def get_by_version(self, version: str) -> Optional[Dict[str, Any]]:
"""Get the latest quality record for a specific version."""
entries = self.get_all()
matches = [e for e in entries if e.get("dataset_version") == version]
if not matches:
return None
return max(matches, key=lambda e: e.get("timestamp", ""))
def get_latest(self) -> Optional[Dict[str, Any]]:
"""Get the most recent quality record."""
entries = self.get_all()
if not entries:
return None
return max(entries, key=lambda e: e.get("timestamp", ""))
def get_versions(self) -> List[str]:
"""Get all unique dataset versions, in chronological order."""
entries = sorted(self.get_all(), key=lambda e: e.get("timestamp", ""))
seen = set()
versions = []
for e in entries:
v = e.get("dataset_version", "unknown")
if v not in seen:
seen.add(v)
versions.append(v)
return versions
# -- comparison --------------------------------------------------------
def compare_versions(
self,
version_a: str,
version_b: str,
) -> Dict[str, Any]:
"""Compare quality metrics between two dataset versions.
Returns dict with metrics from each version and deltas.
"""
a = self.get_by_version(version_a)
b = self.get_by_version(version_b)
if not a or not b:
return {
"error": f"Missing version data: "
f"{'version_a' if not a else 'version_b'} not found",
"version_a": version_a,
"version_b": version_b,
}
compare_keys = [
"total_examples", "valid_examples", "validity_rate",
"avg_response_length", "duplicate_rate", "near_duplicate_rate",
"topic_diversity", "topic_concentration", "too_short", "too_long",
]
delta = {}
pct_change = {}
for k in compare_keys:
va = a.get(k, 0)
vb = b.get(k, 0)
if isinstance(va, (int, float)) and isinstance(vb, (int, float)):
delta[k] = round(vb - va, 4)
if va != 0:
pct_change[k] = round((vb - va) / abs(va) * 100, 2)
else:
pct_change[k] = 0.0
return {
"version_a": version_a,
"version_b": version_b,
"metrics_a": {k: a.get(k) for k in compare_keys},
"metrics_b": {k: b.get(k) for k in compare_keys},
"delta": delta,
"percent_change": pct_change,
}
# -- regression detection ----------------------------------------------
def detect_regressions(
self,
version_a: str,
version_b: str,
) -> List[Dict[str, Any]]:
"""Detect quality regressions between version_a and version_b.
Returns list of regression dicts, each with:
- metric, old_value, new_value, change, threshold, severity
"""
comparison = self.compare_versions(version_a, version_b)
if "error" in comparison:
return []
regressions: List[Dict[str, Any]] = []
for metric, threshold in self.REGRESSION_THRESHOLDS.items():
pct = comparison.get("percent_change", {}).get(metric, 0)
delta = comparison.get("delta", {}).get(metric, 0)
old_val = comparison.get("metrics_a", {}).get(metric, 0)
new_val = comparison.get("metrics_b", {}).get(metric, 0)
is_regression = False
if metric == "duplicate_rate":
# For duplicate_rate, regression is an absolute increase
if delta > threshold:
is_regression = True
else:
# For others, regression is a percentage decrease
if old_val != 0 and (pct / 100) < threshold:
is_regression = True
if is_regression:
severity = "critical" if abs(pct) > abs(threshold * 100 * 2) else "warning"
regressions.append({
"metric": metric,
"old_value": old_val,
"new_value": new_val,
"change": delta,
"percent_change": pct,
"threshold": threshold,
"severity": severity,
})
return regressions
def check_latest_regressions(self) -> List[Dict[str, Any]]:
"""Compare the two most recent versions and check for regressions."""
versions = self.get_versions()
if len(versions) < 2:
return []
return self.detect_regressions(versions[-2], versions[-1])
# -- formatting --------------------------------------------------------
def format_quality_summary(self) -> str:
"""Format a summary of all dataset quality records."""
entries = sorted(self.get_all(), key=lambda e: e.get("timestamp", ""))
if not entries:
return "No dataset quality records found."
lines: List[str] = []
lines.append("=" * 74)
lines.append(" DATASET QUALITY MONITOR")
lines.append("=" * 74)
lines.append(f" Total records: {len(entries)}")
lines.append(f" Versions tracked: {len(self.get_versions())}")
lines.append("")
# Table header
lines.append("-" * 74)
lines.append(
f" {'Version':<16} {'Total':>6} {'Valid':>6} {'AvgLen':>7} "
f"{'Dup%':>6} {'Divers':>7} {'Conc%':>6}"
)
lines.append(
f" {'-------':<16} {'-----':>6} {'-----':>6} {'------':>7} "
f"{'----':>6} {'------':>7} {'-----':>6}"
)
for e in entries:
ver = e.get("dataset_version", "?")[:15]
total = e.get("total_examples", 0)
valid = e.get("valid_examples", 0)
avg_len = e.get("avg_response_length", 0)
dup = e.get("duplicate_rate", 0) * 100
div = e.get("topic_diversity", 0)
conc = e.get("topic_concentration", 0) * 100
lines.append(
f" {ver:<16} {total:>6} {valid:>6} {avg_len:>7.1f} "
f"{dup:>5.1f}% {div:>7.4f} {conc:>5.1f}%"
)
# Regressions
regressions = self.check_latest_regressions()
if regressions:
lines.append("")
lines.append("-" * 74)
lines.append(" QUALITY REGRESSIONS DETECTED")
lines.append("-" * 74)
for r in regressions:
sev = r["severity"].upper()
lines.append(
f" [{sev}] {r['metric']}: "
f"{r['old_value']} -> {r['new_value']} "
f"({r['percent_change']:+.1f}%)"
)
lines.append("")
lines.append("=" * 74)
return "\n".join(lines)
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