mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
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 typing import Dict, Any
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# Загрузка модели
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modelBagOfHeroes = CatBoostClassifier()
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modelBagOfHeroes.load_model("artifacts/model_bag_of_heroes.cbm")
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# Загрузка порядка фич
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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_BAG: list[str] = load_feature_order("artifacts/feature_order_bag_of_heroes.csv")
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def build_bag_of_heroes_features(payload: Dict[str, Any]) -> pd.DataFrame:
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"""
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Конвертирует payload в bag-of-heroes формат.
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payload содержит:
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- is_first_pick_radiant
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- r_h1, r_h2, r_h3, r_h4, r_h5
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- d_h1, d_h2, d_h3, d_h4, d_h5
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Возвращает DataFrame с колонками:
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- is_first_pick_radiant
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- radiant_hero_{1-145}
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- dire_hero_{1-145}
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"""
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# Получаем героев из payload
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radiant_heroes = []
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dire_heroes = []
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for i in range(1, 6):
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r_hero = payload.get(f"r_h{i}", -1)
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d_hero = payload.get(f"d_h{i}", -1)
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if r_hero and r_hero != -1:
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radiant_heroes.append(int(r_hero))
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if d_hero and d_hero != -1:
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dire_heroes.append(int(d_hero))
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# Создаем словарь признаков
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features = {feat: 0 for feat in FEATURE_ORDER_BAG}
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# Устанавливаем is_first_pick_radiant
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features["is_first_pick_radiant"] = int(payload.get("is_first_pick_radiant", 0))
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# Устанавливаем бинарные признаки для героев Radiant
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for hero_id in radiant_heroes:
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feat_name = f"radiant_hero_{hero_id}"
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if feat_name in features:
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features[feat_name] = 1
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# Устанавливаем бинарные признаки для героев Dire
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for hero_id in dire_heroes:
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feat_name = f"dire_hero_{hero_id}"
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if feat_name in features:
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features[feat_name] = 1
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# Создаем DataFrame с правильным порядком колонок
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return pd.DataFrame([features], columns=FEATURE_ORDER_BAG)
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def predict_bag_of_heroes(payload: Dict[str, Any]) -> Dict[str, float]:
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"""
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Делает предсказание с использованием bag-of-heroes модели.
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Возвращает:
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{
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"radiant_win": вероятность победы Radiant (0-100),
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"dire_win": вероятность победы Dire (0-100)
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}
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"""
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X = build_bag_of_heroes_features(payload)
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proba = modelBagOfHeroes.predict_proba(X)[0, 1]
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radiant_win = round(float(np.clip(proba * 100.0, 0.0, 100.0)))
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dire_win = 100.0 - radiant_win
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return {
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"radiant_win": radiant_win,
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"dire_win": dire_win
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}
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