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198 | def classify_column(
column_name: str,
data_type: str | None = None,
profile: dict | None = None,
metadata: dict | None = None,
business_context: str | dict | None = None,
rules: list[dict] | None = None,
) -> dict:
"""Classify one column using term matching, metadata cues, and business context.
Parameters
----------
column_name : Any
Value used by this callable.
data_type : Any
Value used by this callable.
profile : Any
Value used by this callable.
metadata : Any
Value used by this callable.
business_context : Any
Value used by this callable.
rules : Any
Value used by this callable.
Returns
-------
dict
Structured output produced by this callable.
"""
profile = profile or {}
metadata = metadata or {}
text_parts = [column_name, str(metadata.get("description") or ""), str(metadata.get("business_term") or "")]
if isinstance(business_context, str):
text_parts.append(business_context)
elif isinstance(business_context, dict):
text_parts.extend(str(v) for v in business_context.values())
text_blob = " ".join(p for p in text_parts if p)
matched_terms: list[str] = []
inferred_semantic_type = str(profile.get("inferred_semantic_type") or "").lower()
matched_rule_ids: list[str] = []
profile_signals: dict[str, Any] = {}
business_signals: list[str] = []
best = {"classification": "unknown", "confidence": 0.2, "reason": "No governance signal detected", "action": "review"}
semantic_map = {
"email": ("contact", 0.82, "Profile semantic type indicates contact data"),
"phone": ("contact", 0.8, "Profile semantic type indicates contact data"),
"person_name": ("personal_data", 0.8, "Profile semantic type indicates personal data"),
"identifier": ("identifier", 0.85, "Profile semantic type indicates identifier"),
"amount": ("financial", 0.8, "Profile semantic type indicates financial data"),
"free_text": ("sensitive_free_text", 0.72, "Profile semantic type indicates free text"),
}
if inferred_semantic_type in semantic_map:
cls, conf, reason = semantic_map[inferred_semantic_type]
best = {"classification": cls, "confidence": conf, "reason": reason, "action": DEFAULT_ACTION_BY_CLASSIFICATION.get(cls, "review")}
for category, terms in DEFAULT_CLASSIFICATION_TERMS.items():
matches = _match_terms(text_blob, terms)
if matches:
conf = 0.75 if category in {"identifier", "contact", "financial", "health"} else 0.7
if category == "sensitive_free_text":
conf = 0.68
if conf > best["confidence"]:
best = {"classification": category, "confidence": conf, "reason": f"Name/metadata matched {category} terms", "action": DEFAULT_ACTION_BY_CLASSIFICATION.get(category, "review")}
matched_terms.extend(matches)
avg_len = profile.get("avg_length") or profile.get("average_length")
distinct_pct = profile.get("distinct_pct")
if isinstance(avg_len, (int, float)) and avg_len >= 80:
profile_signals["long_text"] = True
if best["classification"] == "sensitive_free_text":
best["confidence"] = max(best["confidence"], 0.82)
if isinstance(distinct_pct, (int, float)) and distinct_pct >= 95:
profile_signals["high_uniqueness"] = True
if best["classification"] in {"identifier", "contact"}:
best["confidence"] = max(best["confidence"], 0.9)
for rule in rules or []:
patterns = [str(p).lower() for p in (rule.get("patterns") or [])]
if any(_phrase_in_text(p, text_blob) for p in patterns):
matched_rule_ids.append(str(rule.get("rule_id") or ""))
if (rule.get("confidence") or 0) >= best["confidence"]:
best = {
"classification": str(rule.get("classification") or best["classification"]),
"confidence": float(rule.get("confidence") or best["confidence"]),
"reason": str(rule.get("reason") or "Matched custom governance rule"),
"action": str(rule.get("action") or DEFAULT_ACTION_BY_CLASSIFICATION.get(str(rule.get("classification") or "unknown"), "review")),
}
for token in ["student", "staff", "hr", "payroll", "medical"]:
if token in text_blob.lower():
business_signals.append(token)
return {
"column_name": column_name,
"data_type": data_type,
"suggested_classification": best["classification"],
"confidence": max(0.0, min(1.0, float(best["confidence"]))),
"reason": best["reason"],
"evidence": {
"matched_terms": sorted(set(matched_terms)),
"matched_rule_ids": [rid for rid in matched_rule_ids if rid],
"data_type": data_type,
"profile_signals": to_jsonable(profile_signals),
"business_context_signals": sorted(set(business_signals)),
},
"suggested_action": best["action"],
"status": "suggested",
"generated_by": "framework",
"approved_by": None,
"approved_at": None,
}
|