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_pro.py

@@ -0,0 +1,94 @@
+import pandas as pd
+import psycopg2
+from psycopg2.extras import RealDictCursor
+
+def get_db_connection():
+    return psycopg2.connect(
+        host="localhost",
+        port=5432,
+        database="korobka_db",
+        user="postgres",
+        password="postgres"
+    )
+
+print("Загрузка данных из БД...")
+
+conn = get_db_connection()
+
+# Загружаем матчи
+matches_df = pd.read_sql_query("""
+    SELECT id as match_id, radiant_team_id, dire_team_id, radiant_win
+    FROM matches
+    WHERE "source" = 'pro'
+    ORDER BY id
+""", conn)
+
+print(f"Загружено {len(matches_df)} матчей")
+
+# Загружаем детали героев
+details_df = pd.read_sql_query("""
+    SELECT match_id, hero_id, team, players_id, pos
+    FROM details_match
+    WHERE "source" = 'pro'
+    ORDER BY match_id
+""", conn)
+
+print(f"Загружено {len(details_df)} записей героев")
+
+conn.close()
+
+# Формируем слоты героев для каждого матча
+def slots_from_picks(group):
+    # --- Radiant (team = 0) ---
+    r_heroes = group[group['team'] == 0]['hero_id'].tolist()[:5]
+    r_players = group[group['team'] == 0]['players_id'].tolist()[:5]
+    r_pos = group[group['team'] == 0]['pos'].tolist()[:5]
+
+    # --- Dire (team = 1) ---
+    d_heroes = group[group['team'] == 1]['hero_id'].tolist()[:5]
+    d_players = group[group['team'] == 1]['players_id'].tolist()[:5]
+    d_pos = group[group['team'] == 1]['pos'].tolist()[:5]
+
+    row = {}
+    # --- Добавляем 5 слотов для каждой стороны ---
+    for i in range(5):
+        # Герои Radiant / Dire
+        row[f"r_h{i+1}"] = r_heroes[i] if i < len(r_heroes) else -1
+        row[f"d_h{i+1}"] = d_heroes[i] if i < len(d_heroes) else -1
+
+        # Позиции героев
+        row[f"rp_h{i+1}"] = r_pos[i] if i < len(r_pos) else -1
+        row[f"dp_h{i+1}"] = d_pos[i] if i < len(d_pos) else -1
+
+        # Игроки Radiant / Dire
+        row[f"r_p{i+1}"] = r_players[i] if i < len(r_players) else -1
+        row[f"d_p{i+1}"] = d_players[i] if i < len(d_players) else -1
+
+    # Определяем, кто пикал первым (команда 0 = radiant)
+    fp_team = group.iloc[0]['team'] if len(group) > 0 else 0
+    row["is_first_pick_radiant"] = 1 if fp_team == 0 else 0
+
+    return pd.Series(row)
+
+slots_df = details_df.groupby("match_id").apply(slots_from_picks).reset_index()
+
+# Объединяем с информацией о матчах
+dataset = matches_df.merge(slots_df, on="match_id", how="inner")
+
+# Добавляем целевую переменную
+dataset['y'] = dataset['radiant_win'].astype(int)
+
+# Выбираем нужные колонки в правильном порядке
+final_df = dataset[['match_id', 'is_first_pick_radiant',
+                    'r_h1', 'r_h2', 'r_h3', 'r_h4', 'r_h5',
+                    'd_h1', 'd_h2', 'd_h3', 'd_h4', 'd_h5',
+                    'r_p1', 'r_p2', 'r_p3', 'r_p4', 'r_p5',
+                    'd_p1', 'd_p2', 'd_p3', 'd_p4', 'd_p5',
+                    'rp_h1', 'rp_h2', 'rp_h3', 'rp_h4', 'rp_h5',
+                    'dp_h1', 'dp_h2', 'dp_h3', 'dp_h4', 'dp_h5',
+                    'y']]
+
+# Сохраняем
+final_df.to_parquet("data/dataset_from_db.parquet", index=False)
+print(f"Сохранено {len(final_df)} записей в data/dataset_from_db.parquet")
+print(f"Radiant wins: {final_df['y'].sum()}, Dire wins: {len(final_df) - final_df['y'].sum()}")