alias_merge.py

#!/usr/bin/env python3
"""alias_merge.py — group fragmented employer spellings into one history key.

WHY THIS EXISTS (c307, 2026-09-11). The paid product's headline feature is
"hand us your list; day one every name is scored against the 1988-present
archive". That report, the per-employer public pages, and the `history` line
attached to every alert are all built by `warn_watch.dossier_index()`, which
keys on the EXACT lowercased `company_canonical` string. The alert MATCHER,
by contrast, uses token-subset matching (`warn_watch.notice_matches`).

Those two disagree, and the disagreement is worst exactly where it hurts most:

    Boeing files under 22 distinct spellings in the archive, including the
    typos 'Boeing Compnay' and 'Thte Boeing Company'. A buyer watching
    "Boeing" DOES get an alert when 'Boeing - El Paso' files (token subset),
    but the history line on that alert looks up the key 'boeing - el paso'
    and reports "2 prior notices" when the truth is 420 across 13 states.

Measured on the 59,134-row archive (c307):
  - 34,154 distinct canonical keys.
  - Tier 1 (stopword/punctuation only): 415 groups, 467 keys, 663 notices.
  - Tier 2 (prefix absorption, purity >= 0.90): 4,615 keys, 7,238 notices.
  => ~7,900 notices (13% of the archive) gain a materially fuller history.

THE OVER-MERGE HAZARD, AND THE GUARD. Naive prefix absorption folds
'venture stores' into 'venture' and 'marriott international' into 'marriott',
which would attribute unrelated companies to each other ON PUBLIC PAGES --
strictly worse than under-counting, because it looks like evidence. The guard
is token PURITY: for a single-token parent P, the share of distinct canonical
keys containing token P that actually START with P.

    boeing 0.93  verizon 0.94  ames 1.00      -> merged
    meta 0.86  united 0.83  compass 0.78      -> blocked
    sears 0.76  novartis 0.69  general 0.63   -> blocked
    marriott 0.47  venture 0.21               -> blocked

At PURITY_MIN = 0.90 the rule deliberately UNDER-merges (it blocks the
genuinely-correct 'cvs health' -> 'cvs'). That asymmetry is intentional: a
missed merge under-reports a history, a false merge publishes a lie.

Multi-token parents ('thermo fisher', 'general dynamics', 'p f chang s') are
not purity-gated -- two or more tokens matching in order is already strong
evidence, and no false merge was found among them by inspection.

USAGE
    python3 alias_merge.py --selftest      # assert the known good/bad cases
    python3 alias_merge.py --report        # impact summary + examples
    from alias_merge import build_alias_map
    amap = build_alias_map(rows)           # exact canonical (lower) -> group key

NOT YET WIRED INTO publish.sh. See BACKLOG 0-ALIAS-MERGE.
"""

import collections
import csv
import re
import sys

FULL_CSV = "out/full/warn_notices.csv"

STOP = {"inc", "llc", "corp", "corporation", "co", "company", "ltd", "the",
        "of", "and", "incorporated", "lp", "llp", "plc"}

PURITY_MIN = 0.90      # single-token parents below this are never used
PARENT_MIN_NOTICES = 3  # a parent must itself be a real, repeatedly-filing employer


def tokens(s):
    """Same tokenizer as warn_watch.tokens -- keep these two in step."""
    return [t for t in re.split(r"[^a-z0-9]+", str(s or "").lower()) if t]


def norm_sig(s):
    """Normalized token signature: lowercase, punctuation-free, stopwords dropped."""
    return tuple(t for t in tokens(s) if t not in STOP)


def canon_of(row):
    return (row.get("company_canonical") or row.get("company") or "").strip().lower()


def build_alias_map(rows, purity_min=PURITY_MIN, parent_min=PARENT_MIN_NOTICES):
    """exact lowercased canonical -> group key (also a lowercased canonical).

    Identity entries are omitted; callers should treat a missing key as
    'maps to itself'. Deterministic: no dict-ordering dependence.
    """
    counts = collections.Counter()
    for r in rows:
        c = canon_of(r)
        if c:
            counts[c] += 1

    # signature -> [canonical keys], and signature total notice count
    by_sig = collections.defaultdict(list)
    for k in counts:
        by_sig[norm_sig(k)].append(k)
    sig_total = {s: sum(counts[k] for k in ks) for s, ks in by_sig.items() if s}

    # token -> distinct signatures containing it (for the purity guard)
    tok2sigs = collections.defaultdict(set)
    for s in sig_total:
        for t in set(s):
            tok2sigs[t].add(s)

    parents = {s for s, n in sig_total.items() if n >= parent_min}

    def purity(p):
        if len(p) != 1:
            return 1.0
        sigs = tok2sigs.get(p[0]) or ()
        if not sigs:
            return 0.0
        return sum(1 for s in sigs if s[:1] == p) / len(sigs)

    # Tier 1: every key in a signature group collapses onto that group's
    # highest-volume key (ties broken by the shorter, then lexically first name).
    def rep_of(keys):
        return sorted(keys, key=lambda k: (-counts[k], len(k), k))[0]

    sig_rep = {s: rep_of(ks) for s, ks in by_sig.items() if s}

    # Tier 2: a signature absorbs into its SHORTEST existing parent prefix.
    absorb = {}
    for s in sig_total:
        for L in range(1, len(s)):
            p = s[:L]
            if p in parents and p != s and purity(p) >= purity_min:
                absorb[s] = p
                break

    # Resolve prefix chains (a -> b -> c) to their terminal parent.
    def resolve(s, _seen=None):
        _seen = _seen or set()
        while s in absorb and s not in _seen:
            _seen.add(s)
            s = absorb[s]
        return s

    amap = {}
    for s, ks in by_sig.items():
        if not s:
            continue
        target = sig_rep[resolve(s)]
        for k in ks:
            if k != target:
                amap[k] = target
    return amap


def load_rows(path=FULL_CSV):
    with open(path, newline="", encoding="utf-8", errors="replace") as f:
        return list(csv.DictReader(f))


# --------------------------------------------------------------------------
# selftest: the cases that justified the design. If these ever flip, the
# tokenizer, the purity guard or the archive changed -- investigate, do not
# "fix" the assertion.
# --------------------------------------------------------------------------
MUST_MERGE = [                       # (child spelling, expected group token)
    ("boeing - el paso", "boeing"),
    ("boeing compnay", "boeing"),
    ("boeing commercial airplane group", "boeing"),
    ("boeing company - oregon location", "boeing"),
]
MUST_NOT_MERGE = [                   # over-merge hazards: must stay separate
    ("venture stores", "venture"),
    ("marriott international", "marriott"),
    ("compass group", "compass"),
    ("sears holdings", "sears"),
]


def selftest():
    rows = load_rows()
    amap = build_alias_map(rows)
    ok = True

    for child, parent_tok in MUST_MERGE:
        got = amap.get(child, child)
        if norm_sig(got) != (parent_tok,):
            print("FAIL merge: %r -> %r (wanted group %r)" % (child, got, parent_tok))
            ok = False

    for child, parent_tok in MUST_NOT_MERGE:
        got = amap.get(child, child)
        if norm_sig(got) == (parent_tok,):
            print("FAIL over-merge: %r was folded into %r" % (child, parent_tok))
            ok = False

    # No key may map to itself, and every target must be a real canonical.
    canon = {canon_of(r) for r in rows}
    for k, v in amap.items():
        if k == v:
            print("FAIL identity entry: %r" % k); ok = False; break
        if v not in canon:
            print("FAIL target not a real employer: %r -> %r" % (k, v)); ok = False; break

    # Boeing's grouped history must beat the ungrouped one.
    grouped = collections.Counter()
    for r in rows:
        c = canon_of(r)
        if c:
            grouped[amap.get(c, c)] += 1
    before = sum(1 for r in rows if canon_of(r) == "boeing")
    after = grouped.get("boeing", 0)
    states = {r.get("state") for r in rows
              if amap.get(canon_of(r), canon_of(r)) == "boeing"}
    print("boeing notices: %d -> %d | states: %d" % (before, after, len(states)))
    if after <= before:
        print("FAIL: grouping did not improve Boeing"); ok = False

    print("alias_merge selftest:", "PASS" if ok else "FAIL")
    return 0 if ok else 1


def report():
    rows = load_rows()
    amap = build_alias_map(rows)
    canon = collections.Counter(canon_of(r) for r in rows if canon_of(r))
    moved = sum(canon[k] for k in amap)
    groups = collections.defaultdict(list)
    for k, v in amap.items():
        groups[v].append(k)
    print("distinct canonical employers : %d" % len(canon))
    print("keys folded into a group     : %d" % len(amap))
    print("notices re-homed             : %d (%.1f%% of %d)"
          % (moved, 100.0 * moved / max(1, len(rows)), len(rows)))
    print("employers whose history grows: %d" % len(groups))
    print("\nlargest regrouped employers:")
    for tgt, ks in sorted(groups.items(),
                          key=lambda kv: -sum(canon[k] for k in kv[1]))[:12]:
        print("  %-42s +%4d notices from %d spellings"
              % (tgt[:42], sum(canon[k] for k in ks), len(ks)))
    return 0


if __name__ == "__main__":
    if "--selftest" in sys.argv:
        sys.exit(selftest())
    sys.exit(report())