"""
MathLingua — Feature Engineering Module

Computes Language Dependency Score (LDS) and Math Confidence Score (MCS)
from student interaction data. These two engineered features disentangle
linguistic struggle from mathematical difficulty, enabling the adaptive
engine to make targeted decisions.

Reference: MathLingua Technical Specification §5
"""

from __future__ import annotations

import math
from dataclasses import dataclass, field
from typing import Optional


# ────────────────────────────────────────────────────────
# Data containers
# ────────────────────────────────────────────────────────

@dataclass
class InteractionSignals:
    """Raw signals captured from a single student-question interaction."""

    max_hint_level: int = 0          # 0 = no hints, 1 = L1, ..., 4 = L4
    time_before_first_hint: float = 0.0   # seconds
    total_time: float = 0.0          # seconds (from display to submission)
    time_at_L1: float = 0.0         # seconds spent at each scaffold
    time_at_L2: float = 0.0
    time_at_L3: float = 0.0
    time_at_L4: float = 0.0
    num_attempts: int = 1            # answer attempts
    is_correct: bool = False
    question_level: str = "1.1"      # difficulty sub-level


@dataclass
class EngineeredFeatures:
    """Output of the feature engineering pipeline for one interaction."""

    # Sub-features for LDS
    hint_depth_normalized: float = 0.0    # D_hint  ∈ [0, 1]
    scaffold_time_ratio: float = 0.0      # R_scaffold ∈ [0, 1]
    escalation_speed: float = 0.0         # E_speed ∈ [0, 1]
    reveal_flag: float = 0.0              # F_reveal ∈ {0, 1}

    # Sub-features for MCS
    correctness: float = 0.0              # C_correct ∈ {0, 1}
    speed_factor: float = 0.0             # S_speed ∈ [0, 1]
    attempt_efficiency: float = 0.0       # A_efficiency ∈ [0, 1]

    # Composite scores
    lds: float = 0.0                      # Language Dependency Score [0, 1]
    mcs: float = 0.0                      # Math Confidence Score [0, 1]

    # Diagnostic quadrant
    quadrant: str = ""                    # thriving | language_gap | math_struggle | dual_challenge


# ────────────────────────────────────────────────────────
# Default median times per level (seconds)
# Calibrated from spec: lower levels → shorter, higher → longer
# ────────────────────────────────────────────────────────

DEFAULT_MEDIAN_TIMES: dict[str, float] = {
    "1.1": 30.0,  "1.2": 35.0,  "1.3": 40.0,  "1.4": 45.0,  "1.5": 50.0,
    "2.1": 55.0,  "2.2": 60.0,  "2.3": 65.0,  "2.4": 70.0,  "2.5": 75.0,
    "3.1": 80.0,  "3.2": 85.0,  "3.3": 90.0,  "3.4": 95.0,  "3.5": 100.0,
}


# ────────────────────────────────────────────────────────
# Feature Engineer
# ────────────────────────────────────────────────────────

class FeatureEngineer:
    """
    Computes LDS and MCS from raw interaction signals.

    LDS = clamp(0.35·D_hint + 0.25·R_scaffold + 0.20·E_speed + 0.20·F_reveal, 0, 1)
    MCS = clamp(0.30·C_correct + 0.25·S_speed + 0.20·A_efficiency + 0.25·(1-LDS), 0, 1)

    The 2×2 diagnostic quadrant is derived from thresholds:
      LDS < 0.4 & MCS ≥ 0.6 → Thriving
      LDS ≥ 0.4 & MCS ≥ 0.6 → Language Gap
      LDS < 0.4 & MCS < 0.6 → Math Struggle
      LDS ≥ 0.4 & MCS < 0.6 → Dual Challenge
    """

    # LDS weights
    W1: float = 0.35   # hint depth
    W2: float = 0.25   # scaffold time ratio
    W3: float = 0.20   # escalation speed
    W4: float = 0.20   # reveal flag

    # MCS weights
    W5: float = 0.30   # correctness
    W6: float = 0.25   # speed factor
    W7: float = 0.20   # attempt efficiency
    W8: float = 0.25   # language independence (1 - LDS)

    # Diagnostic thresholds
    LDS_THRESHOLD: float = 0.4
    MCS_THRESHOLD: float = 0.6

    def __init__(self, median_times: Optional[dict[str, float]] = None):
        self.median_times = median_times or DEFAULT_MEDIAN_TIMES

    @staticmethod
    def _clamp(value: float, lo: float = 0.0, hi: float = 1.0) -> float:
        return max(lo, min(hi, value))

    # ── Sub-feature computations ──

    def _hint_depth_normalized(self, signals: InteractionSignals) -> float:
        """D_hint = h_i / 4"""
        return signals.max_hint_level / 4.0

    def _scaffold_time_ratio(self, signals: InteractionSignals) -> float:
        """R_scaffold = scaffold_time / total_time"""
        scaffold_time = (
            signals.time_at_L1 + signals.time_at_L2 +
            signals.time_at_L3 + signals.time_at_L4
        )
        if signals.total_time <= 0:
            return 0.0
        return self._clamp(scaffold_time / signals.total_time)

    def _escalation_speed(self, signals: InteractionSignals) -> float:
        """E_speed = 1 - (t_pre / median_time) if hints used, else 0"""
        if signals.max_hint_level == 0:
            return 0.0
        median = self.median_times.get(signals.question_level, 60.0)
        if median <= 0:
            return 1.0
        raw = 1.0 - (signals.time_before_first_hint / median)
        return self._clamp(raw)

    def _reveal_flag(self, signals: InteractionSignals) -> float:
        """F_reveal = 1.0 if L4 accessed, else 0.0"""
        return 1.0 if signals.max_hint_level == 4 else 0.0

    def _correctness(self, signals: InteractionSignals) -> float:
        """C_correct ∈ {0, 1}"""
        return 1.0 if signals.is_correct else 0.0

    def _speed_factor(self, signals: InteractionSignals) -> float:
        """S_speed = clamp(median_time / total_time, 0, 1)"""
        median = self.median_times.get(signals.question_level, 60.0)
        if signals.total_time <= 0:
            return 0.0
        return self._clamp(median / signals.total_time)

    def _attempt_efficiency(self, signals: InteractionSignals) -> float:
        """A_efficiency = 1 / attempts"""
        if signals.num_attempts <= 0:
            return 0.0
        return 1.0 / signals.num_attempts

    # ── Composite scores ──

    def _compute_lds(self, d_hint: float, r_scaffold: float,
                     e_speed: float, f_reveal: float) -> float:
        raw = (self.W1 * d_hint + self.W2 * r_scaffold +
               self.W3 * e_speed + self.W4 * f_reveal)
        return self._clamp(raw)

    def _compute_mcs(self, c_correct: float, s_speed: float,
                     a_efficiency: float, lds: float) -> float:
        raw = (self.W5 * c_correct + self.W6 * s_speed +
               self.W7 * a_efficiency + self.W8 * (1.0 - lds))
        return self._clamp(raw)

    def _classify_quadrant(self, lds: float, mcs: float) -> str:
        if lds < self.LDS_THRESHOLD and mcs >= self.MCS_THRESHOLD:
            return "thriving"
        elif lds >= self.LDS_THRESHOLD and mcs >= self.MCS_THRESHOLD:
            return "language_gap"
        elif lds < self.LDS_THRESHOLD and mcs < self.MCS_THRESHOLD:
            return "math_struggle"
        else:
            return "dual_challenge"

    # ── Main entry point ──

    def compute(self, signals: InteractionSignals) -> EngineeredFeatures:
        """Compute all engineered features from raw interaction signals."""

        d_hint = self._hint_depth_normalized(signals)
        r_scaffold = self._scaffold_time_ratio(signals)
        e_speed = self._escalation_speed(signals)
        f_reveal = self._reveal_flag(signals)

        c_correct = self._correctness(signals)
        s_speed = self._speed_factor(signals)
        a_efficiency = self._attempt_efficiency(signals)

        lds = self._compute_lds(d_hint, r_scaffold, e_speed, f_reveal)
        mcs = self._compute_mcs(c_correct, s_speed, a_efficiency, lds)
        quadrant = self._classify_quadrant(lds, mcs)

        return EngineeredFeatures(
            hint_depth_normalized=round(d_hint, 4),
            scaffold_time_ratio=round(r_scaffold, 4),
            escalation_speed=round(e_speed, 4),
            reveal_flag=f_reveal,
            correctness=c_correct,
            speed_factor=round(s_speed, 4),
            attempt_efficiency=round(a_efficiency, 4),
            lds=round(lds, 4),
            mcs=round(mcs, 4),
            quadrant=quadrant,
        )

    def compute_weighted_outcome(self, is_correct: bool,
                                  max_hint_level: int) -> float:
        """
        Hint-weighted outcome for Elo/BKT updates.
        1.00 = correct, no hints
        0.75 = correct, L1 only
        0.50 = correct, L2
        0.25 = correct, L3
        0.00 = incorrect, or L4 used
        """
        if not is_correct or max_hint_level == 4:
            return 0.0
        outcome_map = {0: 1.0, 1: 0.75, 2: 0.50, 3: 0.25}
        return outcome_map.get(max_hint_level, 0.0)


# ────────────────────────────────────────────────────────
# Self-test / examples
# ────────────────────────────────────────────────────────

def _run_examples():
    fe = FeatureEngineer()

    print("=" * 70)
    print("MathLingua Feature Engineering — Worked Examples")
    print("=" * 70)

    # Example 1: Strong student, no hints, fast solve
    signals1 = InteractionSignals(
        max_hint_level=0,
        time_before_first_hint=0.0,
        total_time=25.0,
        is_correct=True,
        num_attempts=1,
        question_level="2.1",
    )
    f1 = fe.compute(signals1)
    print(f"\nExample 1 — Strong student, no hints, fast solve")
    print(f"  LDS = {f1.lds:.3f}  (expected ~0.0)")
    print(f"  MCS = {f1.mcs:.3f}  (expected ~1.0)")
    print(f"  Quadrant: {f1.quadrant}")
    print(f"  Weighted outcome: {fe.compute_weighted_outcome(True, 0)}")

    # Example 2: Language-dependent, used L3, correct
    signals2 = InteractionSignals(
        max_hint_level=3,
        time_before_first_hint=5.0,
        total_time=90.0,
        time_at_L1=10.0,
        time_at_L2=15.0,
        time_at_L3=30.0,
        is_correct=True,
        num_attempts=2,
        question_level="2.3",
    )
    f2 = fe.compute(signals2)
    print(f"\nExample 2 — Language-dependent, used L3, correct on 2nd try")
    print(f"  LDS = {f2.lds:.3f}  (expected ~0.5-0.6)")
    print(f"  MCS = {f2.mcs:.3f}  (expected ~0.3-0.4)")
    print(f"  Quadrant: {f2.quadrant}")
    print(f"  Weighted outcome: {fe.compute_weighted_outcome(True, 3)}")

    # Example 3: Perfect student — fast, correct, no hints
    signals3 = InteractionSignals(
        max_hint_level=0,
        total_time=15.0,
        is_correct=True,
        num_attempts=1,
        question_level="1.1",
    )
    f3 = fe.compute(signals3)
    print(f"\nExample 3 — Perfect interaction (very easy level)")
    print(f"  LDS = {f3.lds:.3f}  (expected 0.0)")
    print(f"  MCS = {f3.mcs:.3f}  (expected 1.0)")
    print(f"  Quadrant: {f3.quadrant}")

    # Example 4: Struggling — used L4, incorrect
    signals4 = InteractionSignals(
        max_hint_level=4,
        time_before_first_hint=3.0,
        total_time=120.0,
        time_at_L1=10.0,
        time_at_L2=15.0,
        time_at_L3=20.0,
        time_at_L4=40.0,
        is_correct=False,
        num_attempts=3,
        question_level="3.1",
    )
    f4 = fe.compute(signals4)
    print(f"\nExample 4 — Struggling student, used all scaffolds, incorrect")
    print(f"  LDS = {f4.lds:.3f}  (expected ~0.7-0.9)")
    print(f"  MCS = {f4.mcs:.3f}  (expected ~0.05-0.15)")
    print(f"  Quadrant: {f4.quadrant}")
    print(f"  Weighted outcome: {fe.compute_weighted_outcome(False, 4)}")

    print("\n" + "=" * 70)
    print("All examples computed successfully ✓")
    print("=" * 70)


if __name__ == "__main__":
    _run_examples()