86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
|
from catboost import CatBoostClassifier
|
|||
|
|
import pandas as pd
|
|||
|
|
import numpy as np
|
|||
|
|
from typing import Dict, Any
|
|||
|
|
|
|||
|
|
# Загрузка модели
|
|||
|
|
modelBagOfHeroes = CatBoostClassifier()
|
|||
|
|
modelBagOfHeroes.load_model("artifacts/model_bag_of_heroes.cbm")
|
|||
|
|
|
|||
|
|
# Загрузка порядка фич
|
|||
|
|
def load_feature_order(path: str) -> list[str]:
|
|||
|
|
fo = pd.read_csv(path)
|
|||
|
|
first_col = fo.columns[0]
|
|||
|
|
return fo[first_col].tolist()
|
|||
|
|
|
|||
|
|
FEATURE_ORDER_BAG: list[str] = load_feature_order("artifacts/feature_order_bag_of_heroes.csv")
|
|||
|
|
|
|||
|
|
def build_bag_of_heroes_features(payload: Dict[str, Any]) -> pd.DataFrame:
|
|||
|
|
"""
|
|||
|
|
Конвертирует payload в bag-of-heroes формат.
|
|||
|
|
|
|||
|
|
payload содержит:
|
|||
|
|
- is_first_pick_radiant
|
|||
|
|
- r_h1, r_h2, r_h3, r_h4, r_h5
|
|||
|
|
- d_h1, d_h2, d_h3, d_h4, d_h5
|
|||
|
|
|
|||
|
|
Возвращает DataFrame с колонками:
|
|||
|
|
- is_first_pick_radiant
|
|||
|
|
- radiant_hero_{1-145}
|
|||
|
|
- dire_hero_{1-145}
|
|||
|
|
"""
|
|||
|
|
# Получаем героев из payload
|
|||
|
|
radiant_heroes = []
|
|||
|
|
dire_heroes = []
|
|||
|
|
|
|||
|
|
for i in range(1, 6):
|
|||
|
|
r_hero = payload.get(f"r_h{i}", -1)
|
|||
|
|
d_hero = payload.get(f"d_h{i}", -1)
|
|||
|
|
|
|||
|
|
if r_hero and r_hero != -1:
|
|||
|
|
radiant_heroes.append(int(r_hero))
|
|||
|
|
if d_hero and d_hero != -1:
|
|||
|
|
dire_heroes.append(int(d_hero))
|
|||
|
|
|
|||
|
|
# Создаем словарь признаков
|
|||
|
|
features = {feat: 0 for feat in FEATURE_ORDER_BAG}
|
|||
|
|
|
|||
|
|
# Устанавливаем is_first_pick_radiant
|
|||
|
|
features["is_first_pick_radiant"] = int(payload.get("is_first_pick_radiant", 0))
|
|||
|
|
|
|||
|
|
# Устанавливаем бинарные признаки для героев Radiant
|
|||
|
|
for hero_id in radiant_heroes:
|
|||
|
|
feat_name = f"radiant_hero_{hero_id}"
|
|||
|
|
if feat_name in features:
|
|||
|
|
features[feat_name] = 1
|
|||
|
|
|
|||
|
|
# Устанавливаем бинарные признаки для героев Dire
|
|||
|
|
for hero_id in dire_heroes:
|
|||
|
|
feat_name = f"dire_hero_{hero_id}"
|
|||
|
|
if feat_name in features:
|
|||
|
|
features[feat_name] = 1
|
|||
|
|
|
|||
|
|
# Создаем DataFrame с правильным порядком колонок
|
|||
|
|
return pd.DataFrame([features], columns=FEATURE_ORDER_BAG)
|
|||
|
|
|
|||
|
|
def predict_bag_of_heroes(payload: Dict[str, Any]) -> Dict[str, float]:
|
|||
|
|
"""
|
|||
|
|
Делает предсказание с использованием bag-of-heroes модели.
|
|||
|
|
|
|||
|
|
Возвращает:
|
|||
|
|
{
|
|||
|
|
"radiant_win": вероятность победы Radiant (0-100),
|
|||
|
|
"dire_win": вероятность победы Dire (0-100)
|
|||
|
|
}
|
|||
|
|
"""
|
|||
|
|
X = build_bag_of_heroes_features(payload)
|
|||
|
|
proba = modelBagOfHeroes.predict_proba(X)[0, 1]
|
|||
|
|
|
|||
|
|
radiant_win = round(float(np.clip(proba * 100.0, 0.0, 100.0)))
|
|||
|
|
dire_win = 100.0 - radiant_win
|
|||
|
|
|
|||
|
|
return {
|
|||
|
|
"radiant_win": radiant_win,
|
|||
|
|
"dire_win": dire_win
|
|||
|
|
}
|