"""Feature builder: human-readable inputs -> 133-dim feature vector.

Design principle: we don't fake the speed-dating-specific features (like
partner attractiveness rating, ambition rating from the opposite gender).
These default to population means (0.5 on a 0-1 scale for normalized
features, 5 on a 1-10 scale for raw). The Gottman and survival features —
which dominate SHAP — are computed precisely from the user's answers.

If the loaded feature_columns list contains names we don't know how to
populate, we fill them with 0.5 (a neutral midpoint after normalization)
and log which ones were defaulted. This keeps predictions directionally
correct without pretending to have data we don't have.
"""
from __future__ import annotations

import logging
from dataclasses import dataclass
from typing import Any

import numpy as np

from src import gottman_scorer, survival_scorer

log = logging.getLogger(__name__)


@dataclass
class UserInputs:
    """The 15 questions from the Quick Check form."""
    # Demographics (4)
    age_you: int
    gender_you: str                  # "female" | "male" | "nonbinary" | "prefer_not"
    education_you: str               # "high_school" | "bachelors" | "masters" | "phd" | "other"
    career_you: str                  # free-text category

    # Partner (4)
    age_partner: int
    gender_partner: str
    education_partner: str
    career_partner: str

    # Dynamic (5)
    shared_interests: int            # Gottman: 1-5
    shared_goals: int                # Gottman: 1-5
    repair_after_conflict: int       # Gottman: 1-5
    criticism_frequency: int         # Gottman: 1-5 (higher = more criticism)
    stonewalling: int                # Gottman: 1-5

    # Context (2)
    relationship_type: str           # "love" | "arranged" | "other"
    marriage_number: int             # 1, 2, 3+

    # Optional hidden fields derived/defaulted
    years_together: float = 2.0
    contempt_frequency: int = 2      # Defaulted if not collected directly
    defensiveness: int = 2
    know_inner_world: int = 4
    partner_knows_me: int = 4


# Educational level -> numeric (matches typical speed-dating coding)
EDU_MAP = {
    "high_school": 2,
    "associates": 3,
    "bachelors": 3,
    "masters": 4,
    "phd": 5,
    "doctorate": 5,
    "other": 3,
}

# Gender -> numeric (0 = female, 1 = male, 0.5 = nonbinary/other; matches
# typical binary encoding in legacy datasets)
GENDER_MAP = {
    "female": 0,
    "male": 1,
    "nonbinary": 0.5,
    "prefer_not": 0.5,
}

# Career category -> rough income/education prior
# (These match typical speed-dating field-of-study codes.)
CAREER_MAP = {
    "tech": 8,
    "finance": 7,
    "medicine": 9,
    "academia": 6,
    "law": 7,
    "arts": 4,
    "education": 5,
    "business": 6,
    "other": 5,
}


def _career_score(c: str) -> float:
    key = c.lower().strip().replace(" ", "_")
    return CAREER_MAP.get(key, 5) / 10.0


def _direct_features(u: UserInputs) -> dict[str, float]:
    """Features we can set directly from user input."""
    age_avg = (u.age_you + u.age_partner) / 2.0
    age_diff = abs(u.age_you - u.age_partner)

    return {
        # Age features
        "age": u.age_you,
        "age_o": u.age_partner,
        "d_age": age_diff,
        "age_avg": age_avg,

        # Demographics
        "gender": GENDER_MAP.get(u.gender_you.lower(), 0.5),
        "samerace": 1.0,  # Assumed; not collected in 15-question form
        "race": 0.0,
        "race_o": 0.0,

        # Education
        "goal": 3.0,  # "serious relationship" default
        "field_cd": EDU_MAP.get(u.education_you.lower(), 3),

        # Income proxy via career
        "income": _career_score(u.career_you) * 100000,  # dollar-scaled

        # Meta context
        "marriage_number": u.marriage_number,
        "years_together": u.years_together,
    }


def build(u: UserInputs, feature_columns: list[str]) -> np.ndarray:
    """Build the full feature vector aligned to the model's column order.

    Returns a float32 array of shape (n_features,).
    """
    # Compute engineered features (Gottman + survival)
    gottman = gottman_scorer.score(
        gottman_scorer.GottmanAnswers(
            shared_interests=u.shared_interests,
            shared_goals=u.shared_goals,
            know_inner_world=u.know_inner_world,
            partner_knows_me=u.partner_knows_me,
            repair_after_conflict=u.repair_after_conflict,
            criticism_frequency=u.criticism_frequency,
            contempt_frequency=u.contempt_frequency,
            defensiveness=u.defensiveness,
            stonewalling=u.stonewalling,
        )
    )
    survival = survival_scorer.compute(
        survival_scorer.SurvivalInputs(
            age_you=u.age_you,
            age_partner=u.age_partner,
            marriage_number=u.marriage_number,
            relationship_type=u.relationship_type,
            years_together=u.years_together,
        )
    )
    direct = _direct_features(u)

    # Merge all knowns
    known: dict[str, float] = {}
    known.update(direct)
    known.update(gottman)
    known.update(survival)

    # Build the vector in the exact order the model expects
    vec = np.zeros(len(feature_columns), dtype=np.float32)
    unknowns: list[str] = []
    for i, col in enumerate(feature_columns):
        if col in known:
            vec[i] = float(known[col])
        else:
            # Fallback: try a loose match (e.g., "attr1_1" -> midpoint of 1-10)
            vec[i] = _default_for(col)
            unknowns.append(col)

    if unknowns:
        log.debug("Defaulted %d/%d features to population means: %s...",
                  len(unknowns), len(feature_columns), unknowns[:5])

    return vec


def _default_for(col: str) -> float:
    """Heuristic default for a feature we can't derive from user input.

    Speed-dating features are typically 1-10 scales (rating attributes) or
    0-1 normalized scores. We use 5.0 for rating-style, 0.5 for normalized,
    and 0.0 for binary.
    """
    name = col.lower()
    # Perception/rating features (attr, sinc, intel, fun, amb, shar)
    if any(name.startswith(p) for p in ("attr", "sinc", "intel", "fun", "amb", "shar")):
        return 5.0
    # Importance weight features (sum to ~100 in speed dating)
    if "imp" in name or "pref" in name:
        return 16.67  # ~100/6 across the six attributes
    # Interest-category features (1-10)
    if any(k in name for k in ("sports", "tvsports", "exercise", "dining",
                                "museums", "art", "hiking", "gaming",
                                "clubbing", "reading", "tv", "theater",
                                "movies", "concerts", "music", "shopping",
                                "yoga")):
        return 5.0
    # Binary/flag features
    if name.startswith(("is_", "has_", "same", "dec_")):
        return 0.0
    # Otherwise: neutral midpoint
    return 0.5