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lottosight/core/predictor.py
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2026-05-23 16:18:08 -04:00

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Python

"""
core/predictor.py
-----------------
Six prediction strategies for LottoSight.
Every function accepts game_id and returns:
{"numbers": [int, ...], "bonus": int | None}
where numbers is sorted, length == game.main_count,
all values in 1..main_max, and bonus in 1..bonus_max (or None).
All strategies accept an optional `exclude` keyword argument (set[int])
that removes specific main-ball numbers from consideration. If excluding
those numbers would leave fewer candidates than main_count, the exclusion
is silently ignored (fallback to the full pool).
"""
import random
from collections import Counter
import numpy as np
from db.models import get_all_draws_numbers, get_game_by_id
from core.analyzer import frequency_analysis, gap_analysis, positional_frequency
# ── Internal helpers ──────────────────────────────────────────────────────────
def _safe_pool(game: dict, exclude: set) -> list[int]:
"""Full main-ball pool minus excluded numbers; falls back to full pool if too few."""
full = list(range(1, game["main_max"] + 1))
filtered = [n for n in full if n not in exclude]
return filtered if len(filtered) >= game["main_count"] else full
def _random_ticket(game: dict, exclude: set | None = None) -> dict:
"""Fully random fallback ticket, respecting exclusions."""
pool = _safe_pool(game, exclude or set())
numbers = sorted(random.sample(pool, game["main_count"]))
bonus = random.randint(1, game["bonus_max"]) if game["bonus_count"] > 0 else None
return {"numbers": numbers, "bonus": bonus}
def _random_bonus(game: dict) -> int | None:
return random.randint(1, game["bonus_max"]) if game["bonus_count"] > 0 else None
def _hot_bonus(draws, game: dict) -> int | None:
"""Most frequent historical bonus ball, or random if no data."""
if game["bonus_count"] == 0:
return None
counter = Counter(d["bonus"] for d in draws if d["bonus"] is not None)
if counter:
return counter.most_common(1)[0][0]
return random.randint(1, game["bonus_max"])
def _fill_to_count(chosen: list, game: dict, exclude: set | None = None) -> list:
"""Pad chosen with random unused numbers if fewer than main_count."""
excl = exclude or set()
needed = game["main_count"] - len(chosen)
if needed > 0:
used = set(chosen)
pool = [n for n in range(1, game["main_max"] + 1) if n not in used and n not in excl]
if len(pool) < needed:
pool = [n for n in range(1, game["main_max"] + 1) if n not in used]
chosen = chosen + random.sample(pool, min(needed, len(pool)))
return sorted(chosen[: game["main_count"]])
# ── Strategy 1: Hot Numbers ───────────────────────────────────────────────────
def hot_numbers(game_id: int, last_n: int = 100, exclude: set | None = None) -> dict:
"""
Top main_count most-frequent numbers from the last last_n draws.
Bonus: most frequent historical bonus ball.
Falls back to random if there is no history.
"""
excl = exclude or set()
game = get_game_by_id(game_id)
draws = get_all_draws_numbers(game_id)
if not draws:
return _random_ticket(game, excl)
recent = draws[-last_n:] if last_n and last_n > 0 else draws
counter = Counter()
for draw in recent:
counter.update(draw["numbers"])
top = [n for n, _ in sorted(counter.items(), key=lambda kv: (-kv[1], kv[0]))
if n not in excl]
numbers = _fill_to_count(top[: game["main_count"]], game, excl)
bonus = _hot_bonus(draws, game)
return {"numbers": numbers, "bonus": bonus}
# ── Strategy 2: Due Numbers ───────────────────────────────────────────────────
def due_numbers(game_id: int, exclude: set | None = None) -> dict:
"""
Numbers with the largest gap (most overdue) based on historical frequency.
Falls back to random if there is no history.
"""
excl = exclude or set()
game = get_game_by_id(game_id)
gaps = gap_analysis(game_id)
if not gaps:
return _random_ticket(game, excl)
top = [n for n, _ in sorted(gaps.items(), key=lambda kv: (-kv[1], kv[0]))
if n not in excl]
numbers = _fill_to_count(top[: game["main_count"]], game, excl)
bonus = _random_bonus(game)
return {"numbers": numbers, "bonus": bonus}
# ── Strategy 3: Weighted Random ───────────────────────────────────────────────
def weighted_random(game_id: int, exclude: set | None = None) -> dict:
"""
Random draw with probability proportional to historical frequency.
Numbers that have never appeared receive a minimum weight of 1
so they remain in contention.
"""
excl = exclude or set()
game = get_game_by_id(game_id)
freq = frequency_analysis(game_id)
pool = _safe_pool(game, excl)
weights = np.array([freq.get(n, 1) for n in pool], dtype=float)
weights /= weights.sum()
chosen = np.random.choice(pool, size=game["main_count"], replace=False, p=weights)
bonus = _random_bonus(game)
return {"numbers": sorted(chosen.tolist()), "bonus": bonus}
# ── Strategy 4: Monte Carlo ───────────────────────────────────────────────────
def monte_carlo(game_id: int, simulations: int = 10_000,
exclude: set | None = None) -> dict:
"""
Run `simulations` weighted-random draws; tally how often each number
is selected; return the top main_count by tally count.
"""
excl = exclude or set()
game = get_game_by_id(game_id)
freq = frequency_analysis(game_id)
pool = _safe_pool(game, excl)
weights = np.array([freq.get(n, 1) for n in pool], dtype=float)
weights /= weights.sum()
tally = Counter()
for _ in range(simulations):
ticket = np.random.choice(pool, size=game["main_count"], replace=False, p=weights)
tally.update(ticket.tolist())
top = [n for n, _ in tally.most_common(game["main_count"])]
numbers = _fill_to_count(top, game, excl)
bonus = _random_bonus(game)
return {"numbers": numbers, "bonus": bonus}
# ── Strategy 5: Positional Pick ───────────────────────────────────────────────
def positional_pick(game_id: int, exclude: set | None = None) -> dict:
"""
For each draw position, select the most frequently appearing number
that has not already been chosen for a previous position.
"""
excl = exclude or set()
game = get_game_by_id(game_id)
pos_freq = positional_frequency(game_id)
if not pos_freq or not any(pos_freq.values()):
return _random_ticket(game, excl)
selected = []
used = set()
for pos in range(1, game["main_count"] + 1):
freqs = pos_freq.get(pos, {})
ranked = sorted(freqs.items(), key=lambda kv: (-kv[1], kv[0]))
picked = next((n for n, _ in ranked if n not in used and n not in excl), None)
if picked is None:
available = [n for n in range(1, game["main_max"] + 1)
if n not in used and n not in excl]
if not available:
available = [n for n in range(1, game["main_max"] + 1) if n not in used]
picked = random.choice(available)
selected.append(picked)
used.add(picked)
bonus = _random_bonus(game)
return {"numbers": sorted(selected), "bonus": bonus}
# ── Strategy 6: Quick Pick ────────────────────────────────────────────────────
def quick_pick(game_id: int, exclude: set | None = None) -> dict:
"""
Pure random selection from the full number pool.
Requires no historical draw data.
"""
game = get_game_by_id(game_id)
return _random_ticket(game, exclude or set())