mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
200 lines
7.4 KiB
Python
200 lines
7.4 KiB
Python
from fastapi import APIRouter, Request
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from pydantic import BaseModel, Field
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from typing import Optional, Dict, Any
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from catboost import CatBoostClassifier
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import pandas as pd
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import numpy as np
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from routes.predict_bag_of_heroes import predict_bag_of_heroes
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from routes.predict_with_players import predict_with_players
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router = APIRouter()
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# =========================
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# Загрузка модели
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# =========================
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modelPro = CatBoostClassifier()
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modelPro.load_model("artifacts/model_from_db_pro_v3.cbm")
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# =========================
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# Загрузка порядка фич
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# =========================
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def load_feature_order(path: str) -> list[str]:
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fo = pd.read_csv(path)
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first_col = fo.columns[0]
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return fo[first_col].tolist()
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FEATURE_ORDER_PRO: list[str] = load_feature_order("artifacts/feature_order_db.csv")
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# =========================
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# Дефолты для недостающих фич
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# =========================
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DEFAULTS: Dict[str, Any] = {
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"is_first_pick_radiant": 0,
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# Radiant heroes
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"r_h1": -1, "r_h2": -1, "r_h3": -1, "r_h4": -1, "r_h5": -1,
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# Dire heroes
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"d_h1": -1, "d_h2": -1, "d_h3": -1, "d_h4": -1, "d_h5": -1,
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# # Radiant players
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"r_p1": -1, "r_p2": -1, "r_p3": -1, "r_p4": -1, "r_p5": -1,
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# # Dire players
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"d_p1": -1, "d_p2": -1, "d_p3": -1, "d_p4": -1, "d_p5": -1,
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# Radiant positions
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"rp_h1": -1, "rp_h2": -1, "rp_h3": -1, "rp_h4": -1, "rp_h5": -1,
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# Dire positions
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"dp_h1": -1, "dp_h2": -1, "dp_h3": -1, "dp_h4": -1, "dp_h5": -1,
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}
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# =========================
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# Входная схема
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# =========================
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class DraftPayload(BaseModel):
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# флаг первого пика (0 — Dire first pick/неизвестно, 1 — Radiant first pick)
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is_first_pick_radiant: Optional[int] = Field(default=DEFAULTS["is_first_pick_radiant"])
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# герои (IDs)
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r_h1: Optional[int] = Field(default=DEFAULTS["r_h1"])
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r_h2: Optional[int] = Field(default=DEFAULTS["r_h2"])
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r_h3: Optional[int] = Field(default=DEFAULTS["r_h3"])
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r_h4: Optional[int] = Field(default=DEFAULTS["r_h4"])
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r_h5: Optional[int] = Field(default=DEFAULTS["r_h5"])
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d_h1: Optional[int] = Field(default=DEFAULTS["d_h1"])
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d_h2: Optional[int] = Field(default=DEFAULTS["d_h2"])
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d_h3: Optional[int] = Field(default=DEFAULTS["d_h3"])
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d_h4: Optional[int] = Field(default=DEFAULTS["d_h4"])
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d_h5: Optional[int] = Field(default=DEFAULTS["d_h5"])
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# игроки (IDs)
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r_p1: Optional[int] = Field(default=DEFAULTS["r_p1"])
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r_p2: Optional[int] = Field(default=DEFAULTS["r_p2"])
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r_p3: Optional[int] = Field(default=DEFAULTS["r_p3"])
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r_p4: Optional[int] = Field(default=DEFAULTS["r_p4"])
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r_p5: Optional[int] = Field(default=DEFAULTS["r_p5"])
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d_p1: Optional[int] = Field(default=DEFAULTS["d_p1"])
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d_p2: Optional[int] = Field(default=DEFAULTS["d_p2"])
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d_p3: Optional[int] = Field(default=DEFAULTS["d_p3"])
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d_p4: Optional[int] = Field(default=DEFAULTS["d_p4"])
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d_p5: Optional[int] = Field(default=DEFAULTS["d_p5"])
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# позиции героев (1-5)
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rp_h1: Optional[int] = Field(default=DEFAULTS["rp_h1"])
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rp_h2: Optional[int] = Field(default=DEFAULTS["rp_h2"])
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rp_h3: Optional[int] = Field(default=DEFAULTS["rp_h3"])
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rp_h4: Optional[int] = Field(default=DEFAULTS["rp_h4"])
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rp_h5: Optional[int] = Field(default=DEFAULTS["rp_h5"])
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dp_h1: Optional[int] = Field(default=DEFAULTS["dp_h1"])
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dp_h2: Optional[int] = Field(default=DEFAULTS["dp_h2"])
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dp_h3: Optional[int] = Field(default=DEFAULTS["dp_h3"])
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dp_h4: Optional[int] = Field(default=DEFAULTS["dp_h4"])
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dp_h5: Optional[int] = Field(default=DEFAULTS["dp_h5"])
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# =========================
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# Хелперы
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# =========================
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def build_long_format_input(payload: dict) -> pd.DataFrame:
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"""
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Конвертирует payload в hero+position combination features для модели.
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Создаёт бинарные признаки вида radiant_h{hero_id}_p{position} и dire_h{hero_id}_p{position}
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"""
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features = {}
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# Инициализируем все признаки нулями
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for feat in FEATURE_ORDER_PRO:
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features[feat] = 0
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# Radiant heroes с позициями
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for i in range(1, 6):
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hero_id = int(payload.get(f"r_h{i}", -1))
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position = int(payload.get(f"rp_h{i}", -1))
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if hero_id >= 0 and position >= 0:
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feature_name = f"radiant_h{hero_id}_p{position}"
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if feature_name in features:
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features[feature_name] = 1
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# Dire heroes с позициями
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for i in range(1, 6):
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hero_id = int(payload.get(f"d_h{i}", -1))
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position = int(payload.get(f"dp_h{i}", -1))
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if hero_id >= 0 and position >= 0:
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feature_name = f"dire_h{hero_id}_p{position}"
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if feature_name in features:
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features[feature_name] = 1
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# Создаём DataFrame с одной строкой в правильном порядке
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df = pd.DataFrame([features], columns=FEATURE_ORDER_PRO)
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return df
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def proba_percent(p: float) -> float:
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"""Перевод вероятности в проценты (0..100) с отсечкой."""
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return round(float(np.clip(p * 100.0, 0.0, 100.0)))
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# =========================
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# Роут
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# =========================
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@router.post("/draft/predict")
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async def predict(request: Request):
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body = await request.json()
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# Конвертируем все значения героев, игроков и позиций в int
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for key in body:
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if key.startswith(("r_h", "d_h", "is_first_pick_radiant")):
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if body[key] is not None and body[key] != "":
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try:
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body[key] = int(body[key])
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except (ValueError, TypeError):
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body[key] = -1
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else:
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body[key] = -1
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elif key.startswith(("rp_h", "dp_h")):
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if body[key] == 0:
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body[key] = -1
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else:
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body[key] = -1
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# Hero+position combination предсказание
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X_pro = build_long_format_input(body)
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# Получаем предсказание для матча (одна строка)
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radiant_pro = float(modelPro.predict_proba(X_pro)[0, 1])
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rp = proba_percent(radiant_pro)
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rd = 100.0 - rp
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# Предсказание bag-of-heroes модели
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bag_prediction = predict_bag_of_heroes(body)
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# Предсказание модели с игроками
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players_prediction = predict_with_players(body)
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# Предсказание стекинг модели (ленивый импорт для избежания циклической зависимости)
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try:
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from routes.predict_stacking import predict_stacking
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stacking_prediction = predict_stacking(body)
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except Exception:
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stacking_prediction = {"radiant_win": 50, "dire_win": 50}
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return {
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"pro-with-pos": {
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"radiant_win": rp,
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"dire_win": rd
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},
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"pro": {
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"radiant_win": bag_prediction["radiant_win"],
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"dire_win": bag_prediction["dire_win"]
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},
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"with-players": {
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"radiant_win": players_prediction["radiant_win"],
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"dire_win": players_prediction["dire_win"]
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},
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"stacking": {
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"radiant_win": stacking_prediction["radiant_win"],
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"dire_win": stacking_prediction["dire_win"]
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}
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}
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