Initial commit: добавление проекта predictV1

Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

educationML/build_dataset_with_players.py

@@ -0,0 +1,139 @@
+import psycopg2
+import pandas as pd
+import numpy as np
+
+print("Подключение к базе данных...")
+conn = psycopg2.connect(
+    host="localhost",
+    port=5432,
+    database="korobka_db",
+    user="postgres",
+    password="postgres"
+)
+
+print("Загрузка матчей с известными игроками...")
+
+# Получаем все матчи где есть хотя бы один известный игрок
+query = """
+SELECT
+    m.id as match_id,
+    m.radiant_win,
+    m.leagueid
+FROM matches m
+WHERE EXISTS (
+    SELECT 1
+    FROM details_match dm
+    WHERE dm.match_id = m.id
+    AND dm.players_id IS NOT NULL
+    AND dm.players_id != 0
+)
+ORDER BY m.id
+"""
+
+matches_df = pd.read_sql(query, conn)
+print(f"Найдено матчей: {len(matches_df)}")
+
+# Получаем детали всех этих матчей
+query_details = """
+SELECT
+    dm.match_id,
+    dm.hero_id,
+    dm.team,
+    dm.players_id,
+    dm.pos,
+    dm."order"
+FROM details_match dm
+WHERE dm.match_id IN (
+    SELECT DISTINCT m.id
+    FROM matches m
+    WHERE EXISTS (
+        SELECT 1
+        FROM details_match dm2
+        WHERE dm2.match_id = m.id
+        AND dm2.players_id IS NOT NULL
+        AND dm2.players_id != 0
+    )
+)
+ORDER BY dm.match_id, dm."order"
+"""
+
+details_df = pd.read_sql(query_details, conn)
+conn.close()
+
+print(f"Загружено {len(details_df)} записей деталей")
+
+# Преобразуем в wide-format
+print("\nПреобразование в wide-format...")
+
+rows = []
+
+for match_id, group in details_df.groupby('match_id'):
+    match_info = matches_df[matches_df['match_id'] == match_id].iloc[0]
+
+    row = {
+        'match_id': match_id,
+        'y': int(match_info['radiant_win']),
+        'leagueid': int(match_info['leagueid'])
+    }
+
+    # Radiant (team=0) и Dire (team=1)
+    radiant_picks = group[group['team'] == 0].sort_values('order')
+    dire_picks = group[group['team'] == 1].sort_values('order')
+
+    # Заполняем героев и игроков для Radiant (до 5)
+    for i, (idx, pick) in enumerate(radiant_picks.iterrows(), 1):
+        if i > 5:
+            break
+        row[f'r_h{i}'] = int(pick['hero_id'])
+        row[f'r_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1
+        row[f'rp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1
+
+    # Заполняем пропуски для Radiant
+    for i in range(len(radiant_picks) + 1, 6):
+        row[f'r_h{i}'] = -1
+        row[f'r_p{i}'] = -1
+        row[f'rp_h{i}'] = -1
+
+    # Заполняем героев и игроков для Dire (до 5)
+    for i, (idx, pick) in enumerate(dire_picks.iterrows(), 1):
+        if i > 5:
+            break
+        row[f'd_h{i}'] = int(pick['hero_id'])
+        row[f'd_p{i}'] = int(pick['players_id']) if pd.notna(pick['players_id']) and pick['players_id'] != 0 else -1
+        row[f'dp_h{i}'] = int(pick['pos']) if pd.notna(pick['pos']) else -1
+
+    # Заполняем пропуски для Dire
+    for i in range(len(dire_picks) + 1, 6):
+        row[f'd_h{i}'] = -1
+        row[f'd_p{i}'] = -1
+        row[f'dp_h{i}'] = -1
+
+    rows.append(row)
+
+df = pd.DataFrame(rows)
+
+print(f"Создано {len(df)} записей в wide-format")
+print(f"Radiant wins: {df['y'].sum()} ({df['y'].mean()*100:.1f}%)")
+print(f"Dire wins: {len(df) - df['y'].sum()} ({(1-df['y'].mean())*100:.1f}%)")
+
+# Статистика по игрокам
+player_cols = [f'r_p{i}' for i in range(1, 6)] + [f'd_p{i}' for i in range(1, 6)]
+all_players = []
+for col in player_cols:
+    all_players.extend(df[col][df[col] > 0].tolist())
+
+unique_players = len(set(all_players))
+print(f"\nУникальных игроков в датасете: {unique_players}")
+print(f"Всего записей игроков (не -1): {len(all_players)}")
+
+# Статистика по турнирам
+print(f"\nУникальных турниров (leagueid): {df['leagueid'].nunique()}")
+
+# Сохранение
+output_path = "data/dataset_with_players.parquet"
+df.to_parquet(output_path, index=False)
+print(f"\n✓ Датасет сохранён: {output_path}")
+
+# Пример первых записей
+print("\nПример данных (первые 3 матча):")
+print(df.head(3).to_string())