mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
235 lines
7.7 KiB
Python
235 lines
7.7 KiB
Python
import sys
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sys.path.insert(0, '.')
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import psycopg2
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import pandas as pd
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import numpy as np
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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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from routes.predict_stacking import predict_stacking
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# Подключение к БД
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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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# Получаем случайные матчи с известными игроками
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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 RANDOM()
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LIMIT 100
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"""
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matches_df = pd.read_sql(query, conn)
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print(f"Загружено {len(matches_df)} случайных матчей")
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# Получаем детали этих матчей
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match_ids = matches_df['match_id'].tolist()
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placeholders = ','.join(['%s'] * len(match_ids))
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query_details = f"""
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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 ({placeholders})
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ORDER BY dm.match_id, dm."order"
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"""
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cursor = conn.cursor()
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cursor.execute(query_details, match_ids)
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details_rows = cursor.fetchall()
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conn.close()
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# Преобразуем детали в словарь по match_id
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details_by_match = {}
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for row in details_rows:
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match_id = row[0]
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if match_id not in details_by_match:
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details_by_match[match_id] = []
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details_by_match[match_id].append({
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'hero_id': row[1],
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'team': row[2],
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'players_id': row[3],
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'pos': row[4],
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'order': row[5]
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})
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print(f"Загружено деталей для {len(details_by_match)} матчей\n")
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# Счётчики правильных предсказаний
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correct = {
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'model1': 0,
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'model2': 0,
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'model3': 0,
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'stacking': 0
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}
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# Списки для хранения всех предсказаний (для расчёта AUC)
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predictions = {
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'model1': [],
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'model2': [],
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'model3': [],
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'stacking': []
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}
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actuals = []
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print("Тестирование моделей...")
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print("="*80)
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# Проверяем каждый матч
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for idx, match_row in matches_df.iterrows():
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match_id = match_row['match_id']
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radiant_win = match_row['radiant_win']
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if match_id not in details_by_match:
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continue
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details = details_by_match[match_id]
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# Формируем payload
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payload = {}
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# Разделяем на Radiant и Dire
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radiant_picks = sorted([d for d in details if d['team'] == 0], key=lambda x: x['order'])
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dire_picks = sorted([d for d in details if d['team'] == 1], key=lambda x: x['order'])
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# Заполняем Radiant
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for i in range(5):
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if i < len(radiant_picks):
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payload[f'r_h{i+1}'] = radiant_picks[i]['hero_id']
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payload[f'r_p{i+1}'] = radiant_picks[i]['players_id'] if radiant_picks[i]['players_id'] else -1
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payload[f'rp_h{i+1}'] = radiant_picks[i]['pos'] if radiant_picks[i]['pos'] else -1
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else:
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payload[f'r_h{i+1}'] = -1
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payload[f'r_p{i+1}'] = -1
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payload[f'rp_h{i+1}'] = -1
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# Заполняем Dire
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for i in range(5):
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if i < len(dire_picks):
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payload[f'd_h{i+1}'] = dire_picks[i]['hero_id']
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payload[f'd_p{i+1}'] = dire_picks[i]['players_id'] if dire_picks[i]['players_id'] else -1
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payload[f'dp_h{i+1}'] = dire_picks[i]['pos'] if dire_picks[i]['pos'] else -1
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else:
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payload[f'd_h{i+1}'] = -1
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payload[f'd_p{i+1}'] = -1
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payload[f'dp_h{i+1}'] = -1
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# Предсказания
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try:
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# Модель 1: Heroes + Positions
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X1 = build_long_format_input(payload)
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pred1_proba = float(modelPro.predict_proba(X1)[0, 1])
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pred1 = pred1_proba >= 0.5
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# Модель 2: Bag of Heroes
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X2 = build_bag_of_heroes_features(payload)
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pred2_proba = float(modelBagOfHeroes.predict_proba(X2)[0, 1])
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pred2 = pred2_proba >= 0.5
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# Модель 3: With Players
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X3 = build_player_features(payload)
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pred3_proba = float(modelWithPlayers.predict_proba(X3)[0, 1])
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pred3 = pred3_proba >= 0.5
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# Стекинг
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stack_result = predict_stacking(payload)
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pred_stack_proba = stack_result['radiant_win'] / 100.0
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pred_stack = pred_stack_proba >= 0.5
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# Сохраняем предсказания
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predictions['model1'].append(pred1_proba)
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predictions['model2'].append(pred2_proba)
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predictions['model3'].append(pred3_proba)
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predictions['stacking'].append(pred_stack_proba)
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actuals.append(int(radiant_win))
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# Подсчитываем правильные предсказания
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if pred1 == radiant_win:
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correct['model1'] += 1
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if pred2 == radiant_win:
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correct['model2'] += 1
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if pred3 == radiant_win:
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correct['model3'] += 1
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if pred_stack == radiant_win:
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correct['stacking'] += 1
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# Показываем первые 10 матчей
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if idx < 10:
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actual_str = "Radiant" if radiant_win else "Dire"
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print(f"\nМатч #{idx+1} (ID: {match_id}):")
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print(f" Реальный результат: {actual_str} win")
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print(f" Модель 1: {pred1_proba*100:.1f}% Radiant ({'✓' if pred1 == radiant_win else '✗'})")
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print(f" Модель 2: {pred2_proba*100:.1f}% Radiant ({'✓' if pred2 == radiant_win else '✗'})")
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print(f" Модель 3: {pred3_proba*100:.1f}% Radiant ({'✓' if pred3 == radiant_win else '✗'})")
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print(f" Стекинг: {pred_stack_proba*100:.1f}% Radiant ({'✓' if pred_stack == radiant_win else '✗'})")
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except Exception as e:
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print(f"Ошибка при обработке матча {match_id}: {e}")
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continue
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# Итоговая статистика
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total = len(actuals)
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print("\n" + "="*80)
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print("ИТОГОВАЯ СТАТИСТИКА")
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print("="*80)
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print(f"Всего проверено матчей: {total}\n")
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print("Точность (Accuracy):")
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print(f" Модель 1 (Heroes + Positions): {correct['model1']}/{total} = {correct['model1']/total*100:.2f}%")
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print(f" Модель 2 (Bag of Heroes): {correct['model2']}/{total} = {correct['model2']/total*100:.2f}%")
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print(f" Модель 3 (With Players): {correct['model3']}/{total} = {correct['model3']/total*100:.2f}%")
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print(f" Мета-модель (Stacking): {correct['stacking']}/{total} = {correct['stacking']/total*100:.2f}%")
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# Расчёт AUC
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from sklearn.metrics import roc_auc_score
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print("\nAUC (Area Under ROC Curve):")
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try:
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auc1 = roc_auc_score(actuals, predictions['model1'])
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print(f" Модель 1 (Heroes + Positions): {auc1:.4f}")
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except:
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print(f" Модель 1 (Heroes + Positions): N/A")
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try:
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auc2 = roc_auc_score(actuals, predictions['model2'])
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print(f" Модель 2 (Bag of Heroes): {auc2:.4f}")
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except:
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print(f" Модель 2 (Bag of Heroes): N/A")
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try:
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auc3 = roc_auc_score(actuals, predictions['model3'])
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print(f" Модель 3 (With Players): {auc3:.4f}")
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except:
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print(f" Модель 3 (With Players): N/A")
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try:
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auc_stack = roc_auc_score(actuals, predictions['stacking'])
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print(f" Мета-модель (Stacking): {auc_stack:.4f}")
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except:
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print(f" Мета-модель (Stacking): N/A")
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print("\n" + "="*80)
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