mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
import pandas as pd
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import psycopg2
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from psycopg2.extras import RealDictCursor
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def get_db_connection():
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return psycopg2.connect(
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host="localhost",
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port=5432,
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database="korobka_db",
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user="postgres",
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password="postgres"
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)
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print("Загрузка данных из БД...")
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conn = get_db_connection()
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# Загружаем матчи
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matches_df = pd.read_sql_query("""
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SELECT id as match_id, radiant_team_id, dire_team_id, radiant_win
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FROM matches
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WHERE "source" = 'pro'
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ORDER BY id
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""", conn)
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print(f"Загружено {len(matches_df)} матчей")
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# Загружаем детали героев
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details_df = pd.read_sql_query("""
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SELECT match_id, hero_id, team, players_id, pos
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FROM details_match
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WHERE "source" = 'pro'
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ORDER BY match_id
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""", conn)
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print(f"Загружено {len(details_df)} записей героев")
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conn.close()
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# Формируем слоты героев для каждого матча
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def slots_from_picks(group):
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# --- Radiant (team = 0) ---
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r_heroes = group[group['team'] == 0]['hero_id'].tolist()[:5]
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r_players = group[group['team'] == 0]['players_id'].tolist()[:5]
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r_pos = group[group['team'] == 0]['pos'].tolist()[:5]
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# --- Dire (team = 1) ---
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d_heroes = group[group['team'] == 1]['hero_id'].tolist()[:5]
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d_players = group[group['team'] == 1]['players_id'].tolist()[:5]
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d_pos = group[group['team'] == 1]['pos'].tolist()[:5]
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row = {}
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# --- Добавляем 5 слотов для каждой стороны ---
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for i in range(5):
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# Герои Radiant / Dire
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row[f"r_h{i+1}"] = r_heroes[i] if i < len(r_heroes) else -1
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row[f"d_h{i+1}"] = d_heroes[i] if i < len(d_heroes) else -1
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# Позиции героев
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row[f"rp_h{i+1}"] = r_pos[i] if i < len(r_pos) else -1
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row[f"dp_h{i+1}"] = d_pos[i] if i < len(d_pos) else -1
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# Игроки Radiant / Dire
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row[f"r_p{i+1}"] = r_players[i] if i < len(r_players) else -1
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row[f"d_p{i+1}"] = d_players[i] if i < len(d_players) else -1
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# Определяем, кто пикал первым (команда 0 = radiant)
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fp_team = group.iloc[0]['team'] if len(group) > 0 else 0
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row["is_first_pick_radiant"] = 1 if fp_team == 0 else 0
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return pd.Series(row)
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slots_df = details_df.groupby("match_id").apply(slots_from_picks).reset_index()
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# Объединяем с информацией о матчах
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dataset = matches_df.merge(slots_df, on="match_id", how="inner")
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# Добавляем целевую переменную
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dataset['y'] = dataset['radiant_win'].astype(int)
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# Выбираем нужные колонки в правильном порядке
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final_df = dataset[['match_id', '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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'r_p1', 'r_p2', 'r_p3', 'r_p4', 'r_p5',
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'd_p1', 'd_p2', 'd_p3', 'd_p4', 'd_p5',
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'rp_h1', 'rp_h2', 'rp_h3', 'rp_h4', 'rp_h5',
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'dp_h1', 'dp_h2', 'dp_h3', 'dp_h4', 'dp_h5',
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'y']]
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# Сохраняем
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final_df.to_parquet("data/dataset_from_db.parquet", index=False)
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print(f"Сохранено {len(final_df)} записей в data/dataset_from_db.parquet")
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print(f"Radiant wins: {final_df['y'].sum()}, Dire wins: {len(final_df) - final_df['y'].sum()}")
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