climperor/recommend.py

"""Draft suggestions from qualitative hero relations.

Score candidates for the player's role-queue position using:
  +1 per enemy the candidate counters
  -1 per enemy that counters the candidate
  +1 per already-locked ally synergy

Data: data/relations.json (not OpenDota winrates).
"""

from __future__ import annotations

from pathlib import Path

from common import ROOT
from grid import hero_table
from relations import DEFAULT_RELATIONS, indexes, load_relations

DEFAULT_ROLE_TAGS = {
    "1": ["Carry"],
    "2": ["Carry", "Nuker", "Escape"],
    "3": ["Initiator", "Durable", "Carry"],
    "4": ["Support"],
    "5": ["Support"],
}


def _maps() -> tuple[dict[str, str], dict[str, list[str]]]:
    table = hero_table()
    names = {h["key"]: h["name_loc"] for h in table}
    roles = {h["key"]: list(h.get("roles") or []) for h in table}
    return names, roles


def enemy_keys(confirmed: dict[int, str], self_team: str | None) -> list[str]:
    if self_team == "radiant":
        slots = range(6, 11)
    elif self_team == "dire":
        slots = range(1, 6)
    else:
        return []
    return [confirmed[s] for s in slots if confirmed.get(s)]


def ally_keys(confirmed: dict[int, str], self_team: str | None, self_slot: int | None = None) -> list[str]:
    """Teammates already locked (excludes your own slot)."""
    if self_team == "radiant":
        slots = range(1, 6)
    elif self_team == "dire":
        slots = range(6, 11)
    else:
        return []
    out = []
    for s in slots:
        if self_slot is not None and s == self_slot:
            continue
        if confirmed.get(s):
            out.append(confirmed[s])
    return out


def candidates_for_position(
    position: int | None,
    *,
    roles_by_key: dict[str, list[str]],
    role_tags: dict | None = None,
) -> list[str]:
    tags_map = role_tags or DEFAULT_ROLE_TAGS
    if position is None:
        return []
    wanted = set(tags_map.get(str(position)) or tags_map.get(position) or [])
    if not wanted:
        return []
    out = []
    for key, tags in roles_by_key.items():
        if wanted.intersection(tags):
            out.append(key)
    return out


def suggest_top(
    *,
    position: int | None,
    enemies: list[str],
    allies: list[str] | None = None,
    exclude: set[str] | list[str],
    relations: dict | None = None,
    top_n: int = 3,
    role_tags: dict | None = None,
    **_ignored,
) -> list[dict]:
    """Return Top-N picks from qualitative counters / synergies.

    Extra kwargs (matchups, synergies, min_games) accepted and ignored for
    backward compatibility with older call sites.
    """
    if position is None:
        return []
    allies = list(allies or [])
    enemies = list(enemies or [])
    if not enemies and not allies:
        return []
    rel = relations if relations is not None else load_relations()
    if not rel.get("counters") and not rel.get("synergies"):
        return []

    names, roles_by_key = _maps()
    counters_of, countered_by, synergies_of = indexes(rel)
    exclude_set = {e for e in exclude if e}

    scored: list[tuple[int, str, dict]] = []
    for cand in candidates_for_position(position, roles_by_key=roles_by_key, role_tags=role_tags):
        if cand in exclude_set:
            continue
        beats = []
        beaten_by = []
        with_allies = []
        for ek in enemies:
            for edge in counters_of.get(cand) or []:
                if edge["key"] == ek:
                    beats.append({"enemy": ek, "reason": edge.get("reason") or ""})
            for edge in countered_by.get(cand) or []:
                if edge["key"] == ek:
                    beaten_by.append({"enemy": ek, "reason": edge.get("reason") or ""})
        for ak in allies:
            for edge in synergies_of.get(cand) or []:
                if edge["key"] == ak:
                    with_allies.append({"ally": ak, "reason": edge.get("reason") or ""})
        score = len(beats) - len(beaten_by) + len(with_allies)
        if score == 0 and not beats and not beaten_by and not with_allies:
            continue
        scored.append((score, cand, {
            "beats": beats,
            "beaten_by": beaten_by,
            "with": with_allies,
        }))

    scored.sort(key=lambda t: (t[0], t[1]), reverse=True)
    out = []
    for rank, (score, key, detail) in enumerate(scored[: max(0, top_n)], start=1):
        out.append({
            "key": key,
            "name_loc": names.get(key, key),
            "rank": rank,
            "score": score,
            **detail,
        })
    return out


__all__ = [
    "DEFAULT_RELATIONS",
    "DEFAULT_ROLE_TAGS",
    "ally_keys",
    "candidates_for_position",
    "enemy_keys",
    "load_relations",
    "suggest_top",
]