paper-dynasty-database/app/services/refractor_boost.py

"""Refractor rating boost service (Phase 2).

Pure functions for computing boosted card ratings when a player
reaches a new Refractor tier. Called by the orchestration layer
in apply_tier_boost().

Batter boost: fixed +0.5 to four offensive columns per tier.
Pitcher boost: 1.5 TB-budget priority algorithm per tier.
"""

from decimal import Decimal, ROUND_HALF_UP
import hashlib
import json
import logging

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Batter constants
# ---------------------------------------------------------------------------

BATTER_POSITIVE_DELTAS: dict[str, Decimal] = {
    "homerun": Decimal("0.50"),
    "double_pull": Decimal("0.50"),
    "single_one": Decimal("0.50"),
    "walk": Decimal("0.50"),
}

BATTER_NEGATIVE_DELTAS: dict[str, Decimal] = {
    "strikeout": Decimal("-1.50"),
    "groundout_a": Decimal("-0.50"),
}

# All 22 outcome columns that must sum to 108.
BATTER_OUTCOME_COLUMNS: list[str] = [
    "homerun",
    "bp_homerun",
    "triple",
    "double_three",
    "double_two",
    "double_pull",
    "single_two",
    "single_one",
    "single_center",
    "bp_single",
    "hbp",
    "walk",
    "strikeout",
    "lineout",
    "popout",
    "flyout_a",
    "flyout_bq",
    "flyout_lf_b",
    "flyout_rf_b",
    "groundout_a",
    "groundout_b",
    "groundout_c",
]

# ---------------------------------------------------------------------------
# Pitcher constants
# ---------------------------------------------------------------------------

# (column, tb_cost) pairs in priority order.
PITCHER_PRIORITY: list[tuple[str, int]] = [
    ("double_cf", 2),
    ("double_three", 2),
    ("double_two", 2),
    ("single_center", 1),
    ("single_two", 1),
    ("single_one", 1),
    ("bp_single", 1),
    ("walk", 1),
    ("homerun", 4),
    ("bp_homerun", 4),
    ("triple", 3),
    ("hbp", 1),
]

# All 18 variable outcome columns that must sum to 79.
PITCHER_OUTCOME_COLUMNS: list[str] = [
    "homerun",
    "bp_homerun",
    "triple",
    "double_three",
    "double_two",
    "double_cf",
    "single_two",
    "single_one",
    "single_center",
    "bp_single",
    "hbp",
    "walk",
    "strikeout",
    "flyout_lf_b",
    "flyout_cf_b",
    "flyout_rf_b",
    "groundout_a",
    "groundout_b",
]

# Cross-check columns that are NEVER modified by the boost algorithm.
PITCHER_XCHECK_COLUMNS: list[str] = [
    "xcheck_p",
    "xcheck_c",
    "xcheck_1b",
    "xcheck_2b",
    "xcheck_3b",
    "xcheck_ss",
    "xcheck_lf",
    "xcheck_cf",
    "xcheck_rf",
]

PITCHER_TB_BUDGET = Decimal("1.5")


# ---------------------------------------------------------------------------
# Batter boost
# ---------------------------------------------------------------------------


def apply_batter_boost(ratings_dict: dict) -> dict:
    """Apply one Refractor tier boost to a batter's outcome ratings.

    Adds fixed positive deltas to four offensive columns (homerun, double_pull,
    single_one, walk) while funding that increase by reducing strikeout and
    groundout_a.  A 0-floor is enforced on negative columns: if the full
    reduction cannot be taken, positive deltas are scaled proportionally so that
    the invariant (22 columns sum to 108.0) is always preserved.

    Args:
        ratings_dict: Dict containing at minimum all 22 BATTER_OUTCOME_COLUMNS
            as numeric (int or float) values.

    Returns:
        New dict with the same keys as ratings_dict, with boosted outcome column
        values as floats.  All other keys are passed through unchanged.

    Raises:
        KeyError: If any BATTER_OUTCOME_COLUMNS key is missing from ratings_dict.
    """
    result = dict(ratings_dict)

    # Step 1 — convert the 22 outcome columns to Decimal for precise arithmetic.
    ratings: dict[str, Decimal] = {
        col: Decimal(str(result[col])) for col in BATTER_OUTCOME_COLUMNS
    }

    # Step 2 — apply negative deltas with 0-floor, tracking how much was
    # actually removed versus how much was requested.
    total_requested_reduction = Decimal("0")
    total_actually_reduced = Decimal("0")

    for col, delta in BATTER_NEGATIVE_DELTAS.items():
        requested = abs(delta)
        total_requested_reduction += requested
        actual = min(requested, ratings[col])
        ratings[col] -= actual
        total_actually_reduced += actual

    # Step 3 — check whether any truncation occurred.
    total_truncated = total_requested_reduction - total_actually_reduced

    # Step 4 — scale positive deltas if we couldn't take the full reduction.
    if total_truncated > Decimal("0"):
        # Positive additions must equal what was actually reduced so the
        # 108-sum is preserved.
        total_requested_addition = sum(BATTER_POSITIVE_DELTAS.values())
        if total_requested_addition > Decimal("0"):
            scale = total_actually_reduced / total_requested_addition
        else:
            scale = Decimal("0")
        logger.warning(
            "refractor_boost: batter truncation occurred — "
            "requested_reduction=%.4f actually_reduced=%.4f scale=%.6f",
            float(total_requested_reduction),
            float(total_actually_reduced),
            float(scale),
        )
        # Quantize the first N-1 deltas independently, then assign the last
        # delta as the remainder so the total addition equals
        # total_actually_reduced exactly (no quantize drift across 4 ops).
        pos_cols = list(BATTER_POSITIVE_DELTAS.keys())
        positive_deltas = {}
        running_sum = Decimal("0")
        for col in pos_cols[:-1]:
            scaled = (BATTER_POSITIVE_DELTAS[col] * scale).quantize(
                Decimal("0.000001"), rounding=ROUND_HALF_UP
            )
            positive_deltas[col] = scaled
            running_sum += scaled
        last_delta = total_actually_reduced - running_sum
        positive_deltas[pos_cols[-1]] = max(last_delta, Decimal("0"))
    else:
        positive_deltas = BATTER_POSITIVE_DELTAS

    # Step 5 — apply (possibly scaled) positive deltas.
    for col, delta in positive_deltas.items():
        ratings[col] += delta

    # Write boosted values back as floats.
    for col in BATTER_OUTCOME_COLUMNS:
        result[col] = float(ratings[col])

    return result


# ---------------------------------------------------------------------------
# Pitcher boost
# ---------------------------------------------------------------------------


def apply_pitcher_boost(ratings_dict: dict, tb_budget: float = 1.5) -> dict:
    """Apply one Refractor tier boost to a pitcher's outcome ratings.

    Iterates through PITCHER_PRIORITY in order, converting as many outcome
    chances as the TB budget allows into strikeouts.  The TB cost per chance
    varies by outcome type (e.g. a double costs 2 TB budget units, a single
    costs 1).  The strikeout column absorbs all converted chances.

    X-check columns (xcheck_p through xcheck_rf) are never touched.

    Args:
        ratings_dict: Dict containing at minimum all 18 PITCHER_OUTCOME_COLUMNS
            as numeric (int or float) values.
        tb_budget: Total base budget available for this boost tier.  Defaults
            to 1.5 (PITCHER_TB_BUDGET).

    Returns:
        New dict with the same keys as ratings_dict, with boosted outcome column
        values as floats.  All other keys are passed through unchanged.

    Raises:
        KeyError: If any PITCHER_OUTCOME_COLUMNS key is missing from ratings_dict.
    """
    result = dict(ratings_dict)

    # Step 1 — convert outcome columns to Decimal, set remaining budget.
    ratings: dict[str, Decimal] = {
        col: Decimal(str(result[col])) for col in PITCHER_OUTCOME_COLUMNS
    }
    remaining = Decimal(str(tb_budget))

    # Step 2 — iterate priority list, draining budget.
    for col, tb_cost in PITCHER_PRIORITY:
        if ratings[col] <= Decimal("0"):
            continue

        tb_cost_d = Decimal(str(tb_cost))
        max_chances = remaining / tb_cost_d
        chances_to_take = min(ratings[col], max_chances)

        ratings[col] -= chances_to_take
        ratings["strikeout"] += chances_to_take
        remaining -= chances_to_take * tb_cost_d

        if remaining <= Decimal("0"):
            break

    # Step 3 — warn if budget was not fully spent (rare, indicates all priority
    # columns were already at zero).
    if remaining > Decimal("0"):
        logger.warning(
            "refractor_boost: pitcher TB budget not fully spent — "
            "remaining=%.4f of tb_budget=%.4f",
            float(remaining),
            tb_budget,
        )

    # Write boosted values back as floats.
    for col in PITCHER_OUTCOME_COLUMNS:
        result[col] = float(ratings[col])

    return result


# ---------------------------------------------------------------------------
# Variant hash
# ---------------------------------------------------------------------------


def compute_variant_hash(
    player_id: int,
    evolution_tier: int,
    cosmetics: list[str] | None = None,
) -> int:
    """Compute a stable, deterministic variant identifier for a boosted card.

    Hashes the combination of player_id, evolution_tier, and an optional sorted
    list of cosmetic identifiers to produce a compact integer suitable for use
    as a database variant key.  The result is derived from the first 8 hex
    characters of a SHA-256 digest, so collisions are extremely unlikely in
    practice.

    variant=0 is reserved and will never be returned; any hash that resolves to
    0 is remapped to 1.

    Args:
        player_id: Player primary key.
        evolution_tier: Refractor tier (0–4) the card has reached.
        cosmetics: Optional list of cosmetic tag strings (e.g. special art
            identifiers).  Order is normalised — callers need not sort.

    Returns:
        A positive integer in the range [1, 2^32 - 1].
    """
    inputs = {
        "player_id": player_id,
        "evolution_tier": evolution_tier,
        "cosmetics": sorted(cosmetics or []),
    }
    raw = hashlib.sha256(json.dumps(inputs, sort_keys=True).encode()).hexdigest()
    result = int(raw[:8], 16)
    return result if result != 0 else 1  # variant=0 is reserved