mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
import pandas as pd
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import numpy as np
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import pickle
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from typing import Dict, Any
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from routes.predict import build_long_format_input, modelPro
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from routes.predict_bag_of_heroes import build_bag_of_heroes_features, modelBagOfHeroes
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from routes.predict_with_players import build_player_features, modelWithPlayers
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# Загрузка мета-модели (Logistic Regression)
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with open("artifacts/model_stacking.pkl", 'rb') as f:
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modelStacking = pickle.load(f)
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def predict_stacking(payload: Dict[str, Any]) -> Dict[str, float]:
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"""
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Делает предсказание с использованием стекинг-модели.
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Сначала получает предсказания от всех базовых моделей,
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затем использует их как признаки для мета-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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# === Предсказание модели 1: Heroes + Positions ===
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X_with_pos = build_long_format_input(payload)
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pred1 = float(modelPro.predict_proba(X_with_pos)[0, 1])
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# === Предсказание модели 2: Bag of Heroes ===
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X_bag = build_bag_of_heroes_features(payload)
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pred2 = float(modelBagOfHeroes.predict_proba(X_bag)[0, 1])
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# === Предсказание модели 3: With Players ===
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X_players = build_player_features(payload)
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pred3 = float(modelWithPlayers.predict_proba(X_players)[0, 1])
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# === Мета-модель ===
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X_meta = pd.DataFrame([{
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"pred_with_positions": pred1,
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"pred_bag_of_heroes": pred2,
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"pred_with_players": pred3
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}])
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proba = modelStacking.predict_proba(X_meta)[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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