climperor/pc/recommend.py

"""Draft suggestions from qualitative hero relations + lineup archetypes.

Mark available heroes with 克 / 搭 / 补:
  克 — relation counters, push/global answers, punish enemy gaps
  搭 — synergy with locked allies
  补 — fill ally tag gaps

Role-queue filters by position tags; otherwise all heroes are candidates.
Also returns a short analysis string and per-mark reasons (no AI).

Data: shared/data/relations.json + draft_archetypes rules.
"""

from __future__ import annotations

import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parents[1]))

from draft_archetypes import (
    answer_for_candidate,
    collect_reasons,
    detect_archetypes,
    detect_gaps,
    format_analysis,
    tag_profile,
)
from shared.grid import hero_table
from shared.hero_tags import tags_for_hero
from shared.relations import DEFAULT_RELATIONS, indexes, load_relations

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

# Soft boosts when candidate tags address a prominent enemy profile face.
# Never creates marks alone.
_PROFILE_BOOSTS: dict[str, tuple[str, ...]] = {
    "爆发": ("耐久", "逃生"),
    "推进": ("控制", "先手"),
    "先手": ("逃生", "控制"),
    "控制": ("逃生", "耐久"),
    "核心": ("控制", "先手"),
    "辅助": ("核心", "先手"),
}

_SOFT_BOOST = 0.25
_ARCH_BOOST = 0.5
_GAP_BOOST = 0.35


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


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]:
    """Role-queue filter. position=None means all heroes (non-role queue)."""
    if position is None:
        return list(roles_by_key.keys())
    tags_map = role_tags or DEFAULT_ROLE_TAGS
    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 enemy_profile(enemies: list[str], tags_by_key: dict[str, list[str]] | None = None) -> dict[str, int]:
    """Count Chinese draft tags across locked enemies."""
    if tags_by_key is None:
        _, _, tags_by_key = _maps()
    return tag_profile(enemies, tags_by_key)


def _profile_soft_boost(cand_tags: list[str], profile: dict[str, int]) -> float:
    if not profile or not cand_tags:
        return 0.0
    cand = set(cand_tags)
    boost = 0.0
    for face, n in sorted(profile.items(), key=lambda kv: (-kv[1], kv[0])):
        if n <= 0:
            continue
        wanted = _PROFILE_BOOSTS.get(face)
        if not wanted:
            continue
        if cand.intersection(wanted):
            boost += _SOFT_BOOST * n
    return boost


def _empty_result() -> dict:
    return {
        "enemy_profile": {},
        "ally_profile": {},
        "enemy_archetypes": [],
        "enemy_gaps": [],
        "ally_gaps": [],
        "analysis": "",
        "marks": [],
    }


def suggest_marks(
    *,
    position: int | None,
    enemies: list[str],
    allies: list[str] | None = None,
    exclude: set[str] | list[str],
    relations: dict | None = None,
    top_n: int | None = 0,
    role_tags: dict | None = None,
    min_enemies: int = 1,
    min_heroes_for_gaps: int = 2,
    archetypes_enabled: bool = True,
    **_ignored,
) -> dict:
    """Return 克/搭/补 marks plus lineup analysis.

    Requires at least ``min_enemies`` locked enemies. ``top_n`` None/<=0 means
    no truncation. ``position`` None = non-role queue (all heroes).
    """
    allies = list(allies or [])
    enemies = list(enemies or [])
    if len(enemies) < max(1, int(min_enemies)):
        return _empty_result()

    rel = relations if relations is not None else load_relations()
    has_rel = bool(rel.get("counters") or rel.get("synergies"))
    if not has_rel and not archetypes_enabled:
        return _empty_result()

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

    e_profile = tag_profile(enemies, tags_by_key)
    a_profile = tag_profile(allies, tags_by_key)

    archetypes: list[str] = []
    enemy_gaps: list[str] = []
    ally_gaps: list[str] = []
    if archetypes_enabled:
        archetypes = detect_archetypes(enemies, tags_by_key)
        enemy_gaps = detect_gaps(
            e_profile, hero_count=len(enemies), min_heroes=min_heroes_for_gaps
        )
        ally_gaps = detect_gaps(
            a_profile, hero_count=len(allies), min_heroes=min_heroes_for_gaps
        )

    analysis = format_analysis(
        archetypes=archetypes,
        enemy_gaps=enemy_gaps,
        ally_gaps=ally_gaps,
        ally_count=len(allies),
    )

    scored: list[tuple[float, 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
        cand_tags = tags_by_key.get(cand) or []
        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 ""})

        arch_hits, punish_gaps, fill_gaps = answer_for_candidate(
            cand,
            cand_tags,
            archetypes=archetypes,
            enemy_gaps=enemy_gaps,
            ally_gaps=ally_gaps,
        )

        labels: list[str] = []
        if beats or arch_hits or punish_gaps:
            labels.append("克")
        if with_allies:
            labels.append("搭")
        if fill_gaps:
            labels.append("补")
        if not labels:
            continue

        reasons = collect_reasons(
            names=names,
            beats=beats,
            with_allies=with_allies,
            archetype_hits=arch_hits,
            punish_gaps=punish_gaps,
            fill_gaps=fill_gaps,
        )
        soft = _profile_soft_boost(cand_tags, e_profile)
        score = (
            float(len(beats) - len(beaten_by) + len(with_allies))
            + soft
            + _ARCH_BOOST * len(arch_hits)
            + _GAP_BOOST * (len(punish_gaps) + len(fill_gaps))
        )
        scored.append((score, cand, {
            "labels": labels,
            "beats": beats,
            "beaten_by": beaten_by,
            "with": with_allies,
            "reasons": reasons,
            "score": round(score, 3),
        }))

    scored.sort(key=lambda t: (t[0], t[1]), reverse=True)
    limit = None if top_n is None or int(top_n) <= 0 else int(top_n)
    sliced = scored if limit is None else scored[:limit]
    marks = []
    for rank, (_score, key, detail) in enumerate(sliced, start=1):
        marks.append({
            "key": key,
            "name_loc": names.get(key, key),
            "rank": rank,
            **detail,
        })
    return {
        "enemy_profile": e_profile,
        "ally_profile": a_profile,
        "enemy_archetypes": archetypes,
        "enemy_gaps": enemy_gaps,
        "ally_gaps": ally_gaps,
        "analysis": analysis,
        "marks": marks,
    }


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 | None = 0,
    role_tags: dict | None = None,
    min_enemies: int = 1,
    min_heroes_for_gaps: int = 2,
    archetypes_enabled: bool = True,
    **_ignored,
) -> list[dict]:
    """Compatibility wrapper: return mark list from ``suggest_marks``."""
    return suggest_marks(
        position=position,
        enemies=enemies,
        allies=allies,
        exclude=exclude,
        relations=relations,
        top_n=top_n,
        role_tags=role_tags,
        min_enemies=min_enemies,
        min_heroes_for_gaps=min_heroes_for_gaps,
        archetypes_enabled=archetypes_enabled,
        **_ignored,
    )["marks"]


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