mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
140 lines
4.2 KiB
Python
140 lines
4.2 KiB
Python
import psycopg2
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import pandas as pd
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import numpy as np
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print("Подключение к базе данных...")
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conn = 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 матчей с известными игроками...")
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# Получаем все матчи где есть хотя бы один известный игрок
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query = """
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SELECT
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m.id as match_id,
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m.radiant_win,
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m.leagueid
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FROM matches m
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WHERE EXISTS (
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SELECT 1
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FROM details_match dm
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WHERE dm.match_id = m.id
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AND dm.players_id IS NOT NULL
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AND dm.players_id != 0
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)
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ORDER BY m.id
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"""
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matches_df = pd.read_sql(query, conn)
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print(f"Найдено матчей: {len(matches_df)}")
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# Получаем детали всех этих матчей
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query_details = """
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SELECT
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dm.match_id,
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dm.hero_id,
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dm.team,
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dm.players_id,
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dm.pos,
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dm."order"
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FROM details_match dm
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WHERE dm.match_id IN (
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SELECT DISTINCT m.id
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FROM matches m
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WHERE EXISTS (
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SELECT 1
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FROM details_match dm2
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WHERE dm2.match_id = m.id
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AND dm2.players_id IS NOT NULL
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AND dm2.players_id != 0
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)
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)
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ORDER BY dm.match_id, dm."order"
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"""
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details_df = pd.read_sql(query_details, conn)
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conn.close()
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print(f"Загружено {len(details_df)} записей деталей")
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# Преобразуем в wide-format
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print("\nПреобразование в wide-format...")
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rows = []
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for match_id, group in details_df.groupby('match_id'):
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match_info = matches_df[matches_df['match_id'] == match_id].iloc[0]
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row = {
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'match_id': match_id,
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'y': int(match_info['radiant_win']),
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'leagueid': int(match_info['leagueid'])
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}
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# Radiant (team=0) и Dire (team=1)
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radiant_picks = group[group['team'] == 0].sort_values('order')
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dire_picks = group[group['team'] == 1].sort_values('order')
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# Заполняем героев и игроков для Radiant (до 5)
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for i, (idx, pick) in enumerate(radiant_picks.iterrows(), 1):
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if i > 5:
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break
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row[f'r_h{i}'] = int(pick['hero_id'])
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row[f'r_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1
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row[f'rp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1
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# Заполняем пропуски для Radiant
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for i in range(len(radiant_picks) + 1, 6):
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row[f'r_h{i}'] = -1
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row[f'r_p{i}'] = -1
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row[f'rp_h{i}'] = -1
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# Заполняем героев и игроков для Dire (до 5)
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for i, (idx, pick) in enumerate(dire_picks.iterrows(), 1):
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if i > 5:
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break
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row[f'd_h{i}'] = int(pick['hero_id'])
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row[f'd_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1
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row[f'dp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1
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# Заполняем пропуски для Dire
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for i in range(len(dire_picks) + 1, 6):
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row[f'd_h{i}'] = -1
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row[f'd_p{i}'] = -1
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row[f'dp_h{i}'] = -1
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rows.append(row)
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df = pd.DataFrame(rows)
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print(f"Создано {len(df)} записей в wide-format")
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print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)")
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print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)")
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# Статистика по игрокам
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player_cols = [f'r_p{i}' for i in range(1, 6)] + [f'd_p{i}' for i in range(1, 6)]
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all_players = []
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for col in player_cols:
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all_players.extend(df[col][df[col] > 0].tolist())
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unique_players = len(set(all_players))
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print(f"\nУникальных игроков в датасете: {unique_players}")
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print(f"Всего записей игроков (не -1): {len(all_players)}")
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# Статистика по турнирам
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print(f"\nУникальных турниров (leagueid): {df['leagueid'].nunique()}")
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# Сохранение
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output_path = "data/dataset_with_players.parquet"
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df.to_parquet(output_path, index=False)
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print(f"\n✓ Датасет сохранён: {output_path}")
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# Пример первых записей
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print("\nПример данных (первые 3 матча):")
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print(df.head(3).to_string())
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