mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
132 lines
4.1 KiB
Python
132 lines
4.1 KiB
Python
import os
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import pandas as pd
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import numpy as np
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from catboost import CatBoostClassifier, Pool
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import roc_auc_score
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print("Загрузка датасета...")
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df = pd.read_parquet("data/dataset_from_db.parquet")
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print(f"Всего записей (матчей): {len(df)}")
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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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# --- Создаём признаки на уровне матча ---
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print("\nСоздание признаков...")
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hero_cols_r = [f"r_h{i}" for i in range(1, 6)]
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hero_cols_d = [f"d_h{i}" for i in range(1, 6)]
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pos_cols_r = [f"rp_h{i}" for i in range(1, 6)]
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pos_cols_d = [f"dp_h{i}" for i in range(1, 6)]
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# Создаём признаки: каждый герой на каждой позиции для каждой команды
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# Формат: radiant_{hero_id}_pos_{position}, dire_{hero_id}_pos_{position}
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rows = []
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for idx, row in df.iterrows():
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features = {}
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# Radiant heroes с позициями
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for i in range(5):
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hero_id = int(row[hero_cols_r[i]])
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position = int(row[pos_cols_r[i]])
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if hero_id >= 0 and position >= 0:
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features[f"radiant_h{hero_id}_p{position}"] = 1
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# Dire heroes с позициями
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for i in range(5):
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hero_id = int(row[hero_cols_d[i]])
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position = int(row[pos_cols_d[i]])
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if hero_id >= 0 and position >= 0:
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features[f"dire_h{hero_id}_p{position}"] = 1
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features['y'] = int(row['y'])
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rows.append(features)
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df_features = pd.DataFrame(rows).fillna(0)
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print(f"Создано признаков: {len(df_features.columns) - 1}")
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# Целевая
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y = df_features['y'].astype(int)
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X = df_features.drop('y', axis=1)
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# Разбиение
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X_train, X_test, y_train, y_test = train_test_split(
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X, y,
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test_size=0.2,
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random_state=42,
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stratify=y
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)
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print(f"\nTrain: {len(X_train)} матчей")
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print(f"Test: {len(X_test)} матчей")
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# Обучение
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train_pool = Pool(X_train, y_train)
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test_pool = Pool(X_test, y_test)
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model = CatBoostClassifier(
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iterations=1000,
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learning_rate=0.05,
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depth=5,
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l2_leaf_reg=3,
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min_data_in_leaf=10,
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bootstrap_type="Bayesian",
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bagging_temperature=0.5,
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loss_function="Logloss",
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eval_metric="AUC",
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random_seed=42,
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verbose=50,
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od_type="Iter",
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od_wait=100,
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use_best_model=True
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)
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print("\nНачало обучения...")
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model.fit(train_pool, eval_set=test_pool)
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# Оценка
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best_scores = model.get_best_score()
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train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan)
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test_auc_cb = best_scores.get("validation", {}).get("AUC", np.nan)
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y_train_proba = model.predict_proba(train_pool)[:, 1]
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y_test_proba = model.predict_proba(test_pool)[:, 1]
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train_auc = roc_auc_score(y_train, y_train_proba)
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test_auc = roc_auc_score(y_test, y_test_proba)
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print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}")
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print(f"Recomputed AUC (train/test): {train_auc:.4f} / {test_auc:.4f}")
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# Сохранение
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os.makedirs("artifacts", exist_ok=True)
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model_path = "artifacts/model_from_db_pro_v3.cbm"
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model.save_model(model_path)
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print(f"\nМодель сохранена: {model_path}")
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# Важность (топ-30)
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importance = model.get_feature_importance(train_pool)
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importance_df = (
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pd.DataFrame({"feature": X_train.columns, "importance": importance})
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.sort_values("importance", ascending=False)
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.reset_index(drop=True)
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)
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print("\nВажность признаков (top 30):")
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print(importance_df.head(30).to_string(index=False))
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importance_df.to_csv("artifacts/feature_importance_db.csv", index=False)
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# Сохраняем список всех возможных признаков для инференса
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all_features = sorted(X.columns.tolist())
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pd.DataFrame(all_features, columns=["feature"]).to_csv(
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"artifacts/feature_order_db.csv", index=False
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)
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print(f"Порядок фичей сохранен в artifacts/feature_order_db.csv ({len(all_features)} признаков)")
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