temporal_keys.py

# temporal_keys - attach, serialize, parse and consume temporal keys
#
# Implements the key schema from "The Temporal Context Gap" (position paper draft,
# Sept 2026, section 5.2):
#
#   <KEY time=1997-11 fetch=2014-11 src=news.wire region=uk register=journalistic
#        drift="cell:1850-1970:prison-room|cell:1971-:biological-unit" selfanchor=0.31 />

from __future__ import annotations

from dataclasses import dataclass, field
import re

__version__ = "0.1.0"


def _int_or_none(s):
    try:
        return int(s)
    except (TypeError, ValueError):
        return None


@dataclass
class DriftAnnotation:
    """One flagged drifting term, its era range, and (optionally) the era-local sense."""

    term: str
    year_start: int | None = None
    year_end: int | None = None
    sense: str | None = None

    def encode(self):
        parts = [self.term.replace(":", "").replace("|", "")]
        if self.year_start is not None or self.year_end is not None:
            parts.append(
                ("" if self.year_start is None else str(self.year_start))
                + "-"
                + ("" if self.year_end is None else str(self.year_end))
            )
            if self.sense is not None:
                parts.append(self.sense.replace(":", "").replace("|", ""))
        return ":".join(parts)

    @classmethod
    def decode(cls, s):
        parts = s.split(":")
        if len(parts) == 1:
            return cls(term=parts[0])
        a, b = parts[1].split("-", 1)
        return cls(
            term=parts[0],
            year_start=_int_or_none(a),
            year_end=_int_or_none(b),
            sense=parts[2] if len(parts) > 2 else None,
        )


@dataclass
class TemporalKey:
    """Temporal key attached to one training instance (paper section 5.2).

    All fields are optional; presence/absence is itself informative.
    """

    time: str | None = None      # document creation time (ISO date or ISO month)
    fetch: str | None = None     # crawl / collection time
    src: str | None = None       # source class, e.g. "news.wire"
    region: str | None = None    # locale, e.g. "uk"
    register: str | None = None  # journalistic / personal / legal / fiction ...
    drift: list = field(default_factory=list)
    selfanchor: float | None = None  # density of internal temporal anchoring (paper section 4, Level 1)

    def serialize(self):
        attrs = []
        for name in ("time", "fetch", "src", "region", "register"):
            v = getattr(self, name)
            if v is not None:
                attrs.append(f"{name}={_sanitize(v)}")
        if self.drift:
            attrs.append("drift=\"" + "|".join(a.encode() for a in self.drift) + "\"")
        if self.selfanchor is not None:
            attrs.append(f"selfanchor={self.selfanchor:.2f}")
        return "<KEY " + " ".join(attrs) + " />"

    def prefix(self):
        """K1 regime: prepend the serialized key to the training instance."""
        return self.serialize() + "\n"

    @classmethod
    def parse(cls, s):
        m = re.fullmatch(r"\s*<KEY\s+(.*?)\s*/>\s*", s, flags=re.DOTALL)
        if not m:
            raise ValueError(f"not a serialized TemporalKey: {s!r}")
        attrs = _tokenize_attrs(m.group(1))
        drift = []
        if "drift" in attrs:
            raw = attrs["drift"].strip('"')
            drift = [DriftAnnotation.decode(tok) for tok in raw.split("|") if tok]
        return cls(
            time=attrs.get("time"),
            fetch=attrs.get("fetch"),
            src=attrs.get("src"),
            region=attrs.get("region"),
            register=attrs.get("register"),
            drift=drift,
            selfanchor=float(attrs["selfanchor"]) if "selfanchor" in attrs else None,
        )

    def mask(self, rng, p=0.15):
        """K2 regime: return a copy with each field independently dropped with probability p.

        Training the model to infer masked fields yields an implicit era-classifier;
        the inferred key doubles as a measurement of surviving temporal signal
        (paper section 5.3, K2; experiment E2).
        """
        masked = TemporalKey(
            time=self.time, fetch=self.fetch, src=self.src,
            region=self.region, register=self.register,
            drift=list(self.drift), selfanchor=self.selfanchor,
        )
        if rng.random() < p:
            masked.time = None
        if rng.random() < p:
            masked.fetch = None
        if rng.random() < p:
            masked.src = None
        if rng.random() < p:
            masked.region = None
        if rng.random() < p:
            masked.register = None
        if rng.random() < p:
            masked.selfanchor = None
        if rng.random() < p:
            masked.drift = []
        return masked


def _sanitize(v):
    return str(v).replace(" ", "_").replace('"', "").replace("=", "-")


def _tokenize_attrs(s):
    out = {}
    i = 0
    while i < len(s):
        while i < len(s) and s[i] == " ":
            i += 1
        eq = s.find("=", i)
        if eq == -1:
            break
        name = s[i:eq]
        if s[eq + 1:eq + 2] == '"':
            close = s.find('"', eq + 2)
            val = s[eq + 2:close]
            i = close + 1
        else:
            j = s.find(" ", eq + 1)
            if j == -1:
                j = len(s)
            val = s[eq + 1:j]
            i = j
        out[name] = val
    return out


def load_lsc_drift_data(path):
    """Load drift data in a SemEval-2020-Task-1-style JSON layout:

    [{"term": "cell",
      "senses": [{"year_start": 1850, "year_end": 1970, "sense": "prison-room"},
                 {"year_start": 1971, "year_end": null, "sense": "biological-unit"}]}]

    Any file matching that layout works regardless of its origin; the pipeline
    never assumes a specific detector produced it.
    """
    import json

    with open(path, encoding="utf-8") as f:
        records = json.load(f)
    for rec in records:
        assert "term" in rec and "senses" in rec, f"bad LSC record: {rec!r}"
        for sense in rec["senses"]:
            assert "year_start" in sense and "year_end" in sense, f"bad sense: {sense!r}"
    return records


def annotate_drift(text, lsc_records, doc_year, max_per_doc=8):
    """Flag LSC-tracked terms found in `text`, with the sense local to `doc_year`.

    If doc_year is None, every sense of every matched term is returned.
    """
    found = []
    lowered = text.lower()
    for rec in lsc_records:
        term = rec["term"]
        if not re.search(r"\b" + re.escape(term.lower()) + r"\b", lowered):
            continue
        for sense in rec["senses"]:
            if doc_year is None or _covers(sense, doc_year):
                found.append(
                    DriftAnnotation(
                        term=term,
                        year_start=sense["year_start"],
                        year_end=sense["year_end"],
                        sense=sense.get("sense"),
                    )
                )
        if len(found) >= max_per_doc:
            break
    return found


def _covers(sense, year):
    a = sense["year_start"]
    b = sense["year_end"]
    return (a is None or year >= a) and (b is None or year <= b)


_SELFANCHOR_PATTERNS = [
    re.compile(r"\b(1[5-9]|20)\d{2}\b"),
    re.compile(
        r"\b(january|february|march|april|may|june|july|august|september|october|"
        r"november|december)\s+(1[5-9]|20)\d{2}\b",
        re.IGNORECASE,
    ),
    re.compile(r"\bas of\s+(1[5-9]|20)\d{2}\b", re.IGNORECASE),
]


def selfanchor_density(text, words_per_unit=100):
    """Self-anchoring expressions per 100 words.

    Higher values mean the text carries its own temporal anchor and is
    trustworthy without an external key (paper section 4, Level 1).
    """
    words = text.split()
    if not words:
        return 0.0
    n_hits = sum(len(p.findall(text)) for p in _SELFANCHOR_PATTERNS)
    return round(100.0 * n_hits / max(1, len(words)), 3)


def make_key(text, lsc_records=None, doc_year=None, **scalar_fields):
    """Convenience constructor: scalar fields + drift annotation + selfanchor."""
    return TemporalKey(
        drift=annotate_drift(text, lsc_records or [], doc_year) if lsc_records else [],
        selfanchor=selfanchor_density(text),
        **scalar_fields,
    )