shinkan-jinkendo/backend/planning_exercise_profiles.py

"""
ExerciseMatchProfile / PlanningTargetProfile — Phase-1-Vorselektion Planungs-Übungssuche.

Siehe .claude/docs/working/PLANNING_EXERCISE_SUGGEST_CONTEXT.md §12–§14
"""
from __future__ import annotations

from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Sequence, Set, Tuple

from skill_scoring import (
    ExerciseOccurrence,
    collect_unit_exercise_occurrences,
    fetch_exercise_skills_bulk,
    profile_for_occurrences,
    _skill_link_multiplier,
    DEFAULT_ITEM_MINUTES,
)


def _ids_to_weights(ids: Sequence[int], primary_id: Optional[int] = None) -> Dict[int, float]:
    out: Dict[int, float] = {}
    for raw in ids or []:
        try:
            fid = int(raw)
        except (TypeError, ValueError):
            continue
        if fid < 1:
            continue
        w = 1.0 if primary_id is not None and fid == int(primary_id) else 0.85
        out[fid] = max(out.get(fid, 0.0), w)
    return out


def _merge_weight_maps(*maps: Optional[Dict[int, float]], scale: float = 1.0) -> Dict[int, float]:
    out: Dict[int, float] = {}
    for m in maps:
        if not m:
            continue
        for k, v in m.items():
            try:
                kid = int(k)
                val = float(v) * scale
            except (TypeError, ValueError):
                continue
            if kid < 1 or val <= 0:
                continue
            out[kid] = max(out.get(kid, 0.0), val)
    return out


def _normalize_weight_map(m: Dict[int, float]) -> Dict[int, float]:
    if not m:
        return {}
    mx = max(m.values())
    if mx <= 0:
        return {}
    return {k: v / mx for k, v in m.items() if v > 0}


def weighted_overlap(a: Dict[int, float], b: Dict[int, float]) -> float:
    """Gewichtete Überlappung 0..1 (min-Summe / max-Summe)."""
    if not a or not b:
        return 0.0
    keys = set(a) | set(b)
    num = sum(min(a.get(k, 0.0), b.get(k, 0.0)) for k in keys)
    den = sum(max(a.get(k, 0.0), b.get(k, 0.0)) for k in keys)
    return num / den if den > 0 else 0.0


def gap_coverage(gap: Dict[int, float], candidate: Dict[int, float]) -> float:
    """Anteil der Skill-Lücke, den der Kandidat abdeckt (0..1)."""
    if not gap:
        return 0.0
    total_gap = sum(gap.values())
    if total_gap <= 0:
        return 0.0
    covered = sum(min(gap.get(k, 0.0), candidate.get(k, 0.0)) for k in gap)
    return covered / total_gap


@dataclass
class ExerciseMatchProfile:
    exercise_id: int
    focus_area_ids: Dict[int, float] = field(default_factory=dict)
    style_direction_ids: Dict[int, float] = field(default_factory=dict)
    training_type_ids: Dict[int, float] = field(default_factory=dict)
    target_group_ids: Dict[int, float] = field(default_factory=dict)
    skill_weights: Dict[int, float] = field(default_factory=dict)

    def to_dict(self) -> Dict[str, Any]:
        return {
            "exercise_id": self.exercise_id,
            "focus_area_ids": self.focus_area_ids,
            "style_direction_ids": self.style_direction_ids,
            "training_type_ids": self.training_type_ids,
            "target_group_ids": self.target_group_ids,
            "skill_weights": self.skill_weights,
        }


@dataclass
class PlanningTargetProfile:
    focus_area_ids: Dict[int, float] = field(default_factory=dict)
    style_direction_ids: Dict[int, float] = field(default_factory=dict)
    training_type_ids: Dict[int, float] = field(default_factory=dict)
    target_group_ids: Dict[int, float] = field(default_factory=dict)
    skill_weights: Dict[int, float] = field(default_factory=dict)
    skill_gap_weights: Dict[int, float] = field(default_factory=dict)
    skill_plan_weights: Dict[int, float] = field(default_factory=dict)
    sources: List[str] = field(default_factory=list)

    def to_summary_dict(self, cur, limit_skills: int = 5) -> Dict[str, Any]:
        focus_labels = _load_focus_labels(cur, list(self.focus_area_ids.keys())[:6])
        top_skills = sorted(self.skill_weights.items(), key=lambda x: -x[1])[:limit_skills]
        skill_names = _load_skill_names(cur, [s[0] for s in top_skills])
        return {
            "sources": list(self.sources),
            "focus_areas": focus_labels,
            "top_skills": [
                {"skill_id": sid, "name": skill_names.get(sid, f"#{sid}"), "weight": round(w, 2)}
                for sid, w in top_skills
            ],
            "has_skill_gap": bool(self.skill_gap_weights),
        }


def _load_focus_labels(cur, ids: Sequence[int]) -> List[str]:
    if not ids:
        return []
    ph = ",".join(["%s"] * len(ids))
    cur.execute(
        f"SELECT id, name FROM focus_areas WHERE id IN ({ph}) ORDER BY name",
        list(ids),
    )
    return [f"{r['name'] or r['id']}" for r in cur.fetchall()]


def _load_skill_names(cur, ids: Sequence[int]) -> Dict[int, str]:
    if not ids:
        return {}
    ph = ",".join(["%s"] * len(ids))
    cur.execute(f"SELECT id, name FROM skills WHERE id IN ({ph})", list(ids))
    return {int(r["id"]): str(r["name"] or "") for r in cur.fetchall()}


def _skill_weights_from_profile(skills_out: Sequence[Dict[str, Any]]) -> Dict[int, float]:
    out: Dict[int, float] = {}
    for row in skills_out or []:
        sid = row.get("skill_id")
        if sid is None:
            continue
        w = float(row.get("weight") or row.get("score") or 0)
        if w > 0:
            out[int(sid)] = w
    return out


def _single_exercise_skill_weights(
    skill_rows: Sequence[Dict[str, Any]],
    *,
    minutes: float = DEFAULT_ITEM_MINUTES,
) -> Dict[int, float]:
    out: Dict[int, float] = {}
    for link in skill_rows or []:
        sid = link.get("skill_id")
        if sid is None:
            continue
        sid = int(sid)
        mult = _skill_link_multiplier(
            intensity=link.get("intensity"),
            required_level=link.get("required_level"),
            target_level=link.get("target_level"),
        )
        w = minutes * mult
        if w > 0:
            out[sid] = out.get(sid, 0.0) + w
    return out


def _load_relation_maps_bulk(
    cur,
    exercise_ids: Sequence[int],
    table: str,
    id_column: str,
) -> Dict[int, Dict[int, float]]:
    ids = [int(x) for x in exercise_ids if int(x) > 0]
    if not ids:
        return {}
    ph = ",".join(["%s"] * len(ids))
    cur.execute(
        f"""
        SELECT exercise_id, {id_column} AS rel_id, is_primary
        FROM {table}
        WHERE exercise_id IN ({ph})
        """,
        ids,
    )
    out: Dict[int, Dict[int, float]] = {eid: {} for eid in ids}
    for row in cur.fetchall():
        eid = int(row["exercise_id"])
        rid = int(row["rel_id"])
        w = 1.0 if row.get("is_primary") else 0.85
        out.setdefault(eid, {})[rid] = max(out[eid].get(rid, 0.0), w)
    return out


def load_exercise_match_profiles_bulk(cur, exercise_ids: Sequence[int]) -> Dict[int, ExerciseMatchProfile]:
    ids = sorted({int(x) for x in exercise_ids if int(x) > 0})
    if not ids:
        return {}

    focus_map = _load_relation_maps_bulk(cur, ids, "exercise_focus_areas", "focus_area_id")
    style_map = _load_relation_maps_bulk(cur, ids, "exercise_style_directions", "style_direction_id")
    type_map = _load_relation_maps_bulk(cur, ids, "exercise_training_types", "training_type_id")
    tg_map = _load_relation_maps_bulk(cur, ids, "exercise_target_groups", "target_group_id")
    skills_bulk = fetch_exercise_skills_bulk(cur, ids)

    profiles: Dict[int, ExerciseMatchProfile] = {}
    for eid in ids:
        profiles[eid] = ExerciseMatchProfile(
            exercise_id=eid,
            focus_area_ids=focus_map.get(eid, {}),
            style_direction_ids=style_map.get(eid, {}),
            training_type_ids=type_map.get(eid, {}),
            target_group_ids=tg_map.get(eid, {}),
            skill_weights=_single_exercise_skill_weights(skills_bulk.get(eid, [])),
        )
    return profiles


def _resolve_framework_for_unit(cur, unit: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    slot_id = unit.get("framework_slot_id") or unit.get("origin_framework_slot_id")
    if not slot_id:
        return None
    cur.execute(
        """
        SELECT s.id AS slot_id, s.framework_program_id, s.sort_order, s.title AS slot_title,
               fp.title AS framework_title, fp.focus_area_id AS header_focus_area_id
        FROM training_framework_slots s
        JOIN training_framework_programs fp ON fp.id = s.framework_program_id
        WHERE s.id = %s
        """,
        (int(slot_id),),
    )
    row = cur.fetchone()
    return dict(row) if row else None


def _framework_catalog_weights(cur, framework_id: int) -> Tuple[Dict[int, float], Dict[int, float], Dict[int, float], Dict[int, float]]:
    cur.execute(
        "SELECT focus_area_id FROM training_framework_programs WHERE id = %s",
        (framework_id,),
    )
    hdr = cur.fetchone()
    header_fa = int(hdr["focus_area_id"]) if hdr and hdr.get("focus_area_id") else None

    cur.execute(
        "SELECT focus_area_id FROM training_framework_program_focus_areas WHERE framework_program_id = %s",
        (framework_id,),
    )
    fa_ids = [int(r["focus_area_id"]) for r in cur.fetchall()]
    if header_fa and header_fa not in fa_ids:
        fa_ids.insert(0, header_fa)
    focus = _ids_to_weights(fa_ids, primary_id=header_fa)

    cur.execute(
        "SELECT style_direction_id FROM training_framework_program_style_directions WHERE framework_program_id = %s",
        (framework_id,),
    )
    style = _ids_to_weights([int(r["style_direction_id"]) for r in cur.fetchall()])

    cur.execute(
        "SELECT training_type_id FROM training_framework_program_training_types WHERE framework_program_id = %s",
        (framework_id,),
    )
    tt = _ids_to_weights([int(r["training_type_id"]) for r in cur.fetchall()])

    cur.execute(
        "SELECT target_group_id FROM training_framework_program_target_groups WHERE framework_program_id = %s",
        (framework_id,),
    )
    tg = _ids_to_weights([int(r["target_group_id"]) for r in cur.fetchall()])

    return focus, style, tt, tg


def _profile_from_unit_occurrences(cur, unit_id: int) -> Dict[int, float]:
    occ = collect_unit_exercise_occurrences(cur, int(unit_id))
    if not occ:
        return {}
    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
    return _skill_weights_from_profile(prof.get("skills") or [])


def _profile_from_exercise_ids(cur, exercise_ids: Sequence[int]) -> Dict[int, float]:
    ids = [int(x) for x in exercise_ids if int(x) > 0]
    if not ids:
        return {}
    occ = [ExerciseOccurrence(exercise_id=eid) for eid in ids]
    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
    return _skill_weights_from_profile(prof.get("skills") or [])


def skill_profile_summary_from_exercise_ids(
    cur,
    exercise_ids: Sequence[int],
    *,
    limit_skills: int = 8,
) -> Dict[str, Any]:
    """Kompaktes Fähigkeitenprofil für LLM-Kontext und UI."""
    ids = [int(x) for x in exercise_ids if int(x) > 0]
    if not ids:
        return {"exercise_count": 0, "skills": []}
    occ = [ExerciseOccurrence(exercise_id=eid) for eid in ids]
    prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
    skills_out = prof.get("skills") or []
    top = sorted(skills_out, key=lambda s: -float(s.get("weight") or s.get("score") or 0))[:limit_skills]
    names = _load_skill_names(cur, [int(s["skill_id"]) for s in top if s.get("skill_id") is not None])
    return {
        "exercise_count": len(ids),
        "skills": [
            {
                "skill_id": int(s["skill_id"]),
                "name": names.get(int(s["skill_id"]), f"#{s['skill_id']}"),
                "weight": round(float(s.get("weight") or s.get("score") or 0), 3),
            }
            for s in top
            if s.get("skill_id") is not None
        ],
    }


def build_planning_target_profile(
    cur,
    *,
    unit: Dict[str, Any],
    planned_exercise_ids: Sequence[int],
    section_planned_exercise_ids: Optional[Sequence[int]] = None,
    anchor_exercise_id: Optional[int],
    intent: str,
) -> PlanningTargetProfile:
    sources: List[str] = []
    focus: Dict[int, float] = {}
    style: Dict[int, float] = {}
    tt: Dict[int, float] = {}
    tg: Dict[int, float] = {}
    skill_target: Dict[int, float] = {}
    skill_plan: Dict[int, float] = {}

    fw = _resolve_framework_for_unit(cur, unit)
    if fw:
        fid = int(fw["framework_program_id"])
        f_focus, f_style, f_tt, f_tg = _framework_catalog_weights(cur, fid)
        focus = _merge_weight_maps(focus, f_focus)
        style = _merge_weight_maps(style, f_style)
        tt = _merge_weight_maps(tt, f_tt)
        tg = _merge_weight_maps(tg, f_tg)
        sources.append("framework_catalog")

        slot_id = fw.get("slot_id")
        cur.execute(
            "SELECT id FROM training_units WHERE framework_slot_id = %s LIMIT 1",
            (int(slot_id),),
        )
        bp = cur.fetchone()
        if bp and bp.get("id"):
            slot_skills = _profile_from_unit_occurrences(cur, int(bp["id"]))
            if slot_skills:
                skill_target = _merge_weight_maps(skill_target, slot_skills, scale=1.0)
                sources.append("framework_slot_skill_profile")
        if not skill_target:
            cur.execute(
                """
                SELECT tu.id FROM training_framework_slots s
                LEFT JOIN training_units tu ON tu.framework_slot_id = s.id
                WHERE s.framework_program_id = %s AND tu.id IS NOT NULL
                """,
                (fid,),
            )
            all_occ: List[ExerciseOccurrence] = []
            for r in cur.fetchall():
                all_occ.extend(collect_unit_exercise_occurrences(cur, int(r["id"])))
            if all_occ:
                prof = profile_for_occurrences(cur, all_occ, reference_max_by_skill=None)
                skill_target = _merge_weight_maps(
                    skill_target, _skill_weights_from_profile(prof.get("skills") or []), scale=0.85
                )
                sources.append("framework_overall_skill_profile")

    if planned_exercise_ids:
        occ = [ExerciseOccurrence(exercise_id=int(eid)) for eid in planned_exercise_ids]
        prof = profile_for_occurrences(cur, occ, reference_max_by_skill=None)
        skill_plan = _skill_weights_from_profile(prof.get("skills") or [])
        if skill_plan:
            sources.append("current_unit_plan")

    section_ids = [int(x) for x in (section_planned_exercise_ids or []) if int(x) > 0]
    if section_ids:
        section_skills = _profile_from_exercise_ids(cur, section_ids)
        if section_skills:
            skill_target = _merge_weight_maps(skill_target, section_skills, scale=1.0)
            sources.append("current_section_plan")

    if anchor_exercise_id:
        anchor_profiles = load_exercise_match_profiles_bulk(cur, [int(anchor_exercise_id)])
        ap = anchor_profiles.get(int(anchor_exercise_id))
        if ap:
            if intent in ("deepen_exercise", "suggest_next", "progression_next", "continue_plan_goal"):
                skill_target = _merge_weight_maps(skill_target, ap.skill_weights, scale=1.0)
                focus = _merge_weight_maps(focus, ap.focus_area_ids, scale=0.9)
                style = _merge_weight_maps(style, ap.style_direction_ids, scale=0.75)
                tt = _merge_weight_maps(tt, ap.training_type_ids, scale=0.75)
                tg = _merge_weight_maps(tg, ap.target_group_ids, scale=0.75)
                sources.append("anchor_exercise")

    skill_target = _normalize_weight_map(skill_target)
    skill_plan_norm = _normalize_weight_map(skill_plan)
    skill_gap: Dict[int, float] = {}
    for sid, tw in skill_target.items():
        pw = skill_plan_norm.get(sid, 0.0)
        gap = tw - pw * 0.85
        if gap > 0.08:
            skill_gap[sid] = gap
    if skill_gap:
        sources.append("skill_gap_vs_plan")

    return PlanningTargetProfile(
        focus_area_ids=_normalize_weight_map(focus) if focus else focus,
        style_direction_ids=_normalize_weight_map(style) if style else style,
        training_type_ids=_normalize_weight_map(tt) if tt else tt,
        target_group_ids=_normalize_weight_map(tg) if tg else tg,
        skill_weights=skill_target,
        skill_gap_weights=_normalize_weight_map(skill_gap) if skill_gap else skill_gap,
        skill_plan_weights=skill_plan_norm,
        sources=sources,
    )


def score_exercise_against_target(
    exercise: ExerciseMatchProfile,
    target: PlanningTargetProfile,
    *,
    intent: str,
) -> Tuple[float, List[str]]:
    """Profil-Match 0..1 + deutschsprachige Gründe."""
    reasons: List[str] = []

    focus_sim = weighted_overlap(exercise.focus_area_ids, target.focus_area_ids)
    style_sim = weighted_overlap(exercise.style_direction_ids, target.style_direction_ids)
    tt_sim = weighted_overlap(exercise.training_type_ids, target.training_type_ids)
    tg_sim = weighted_overlap(exercise.target_group_ids, target.target_group_ids)
    skill_sim = weighted_overlap(
        _normalize_weight_map(exercise.skill_weights),
        target.skill_weights,
    )
    gap_sim = gap_coverage(target.skill_gap_weights, _normalize_weight_map(exercise.skill_weights))

    if focus_sim >= 0.5 and target.focus_area_ids:
        reasons.append("Fokusbereich passend zum Planungsziel")
    if style_sim >= 0.5 and target.style_direction_ids:
        reasons.append("Stilrichtung passend")
    if tt_sim >= 0.5 and target.training_type_ids:
        reasons.append("Trainingsstil passend")
    if tg_sim >= 0.5 and target.target_group_ids:
        reasons.append("Zielgruppe passend")
    if skill_sim >= 0.35 and target.skill_weights:
        reasons.append("Fähigkeiten-Schwerpunkt passend (Profilmetrik)")
    if gap_sim >= 0.25 and target.skill_gap_weights:
        reasons.append("Deckt Skill-Lücke im bisherigen Plan")
    if "query_intent" in (target.sources or []):
        reasons.append("Passt zur KI-interpretierten Suchanfrage")

    # Intent-gewichtete Dimensionen (Summe = 1.0)
    if intent == INTENT_FREE_SEARCH:
        weights = {"focus": 0.15, "style": 0.10, "tt": 0.10, "tg": 0.10, "skill": 0.25, "gap": 0.30}
    elif intent == INTENT_DEEPEN_EXERCISE:
        weights = {"focus": 0.15, "style": 0.10, "tt": 0.10, "tg": 0.05, "skill": 0.45, "gap": 0.15}
    elif intent == INTENT_PROGRESSION_NEXT:
        weights = {"focus": 0.20, "style": 0.10, "tt": 0.10, "tg": 0.05, "skill": 0.35, "gap": 0.20}
    else:
        weights = {"focus": 0.20, "style": 0.10, "tt": 0.10, "tg": 0.10, "skill": 0.30, "gap": 0.20}

    score = (
        weights["focus"] * focus_sim
        + weights["style"] * style_sim
        + weights["tt"] * tt_sim
        + weights["tg"] * tg_sim
        + weights["skill"] * skill_sim
        + weights["gap"] * gap_sim
    )
    return max(0.0, min(1.0, score)), reasons


# Re-export intent constants for typing (avoid circular import at runtime in suggest module)
INTENT_FREE_SEARCH = "free_search"
INTENT_DEEPEN_EXERCISE = "deepen_exercise"
INTENT_PROGRESSION_NEXT = "progression_next"