mamonov.ep
8a134239d7
Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
117 lines
4.0 KiB
Python
117 lines
4.0 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: {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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# --- Фичи под новый формат датасета ---
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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, 5+1)]
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# player_cols_r = [f"r_p{i}" for i in range(1, 6)]
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# player_cols_d = [f"d_p{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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feature_cols = (
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["is_first_pick_radiant"]
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+ hero_cols_r + hero_cols_d
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# + player_cols_r + player_cols_d # Убрали игроков - мало данных
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+ pos_cols_r + pos_cols_d
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)
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# Целевая
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target_col = "y"
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# Отделяем признаки/таргет
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X = df[feature_cols].copy()
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y = df[target_col].astype(int).copy()
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# На всякий случай убедимся, что бинарный признак int
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X["is_first_pick_radiant"] = X["is_first_pick_radiant"].astype(int)
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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=0.1,
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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)} записей")
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print(f"Test: {len(X_test)} записей")
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# Категориальные признаки: герои и позиции (их ID — это категории)
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cat_features = hero_cols_r + hero_cols_d + pos_cols_r + pos_cols_d
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# CatBoost принимает либо индексы, либо имена колонок. Передаем имена.
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train_pool = Pool(X_train, y_train, cat_features=cat_features)
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test_pool = Pool(X_test, y_test, cat_features=cat_features)
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# Модель
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model = CatBoostClassifier(
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iterations=2500,
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learning_rate=0.03,
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depth=7,
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l2_leaf_reg=2,
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bootstrap_type="Bayesian",
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bagging_temperature=1.0, # <- вместо subsample
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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=100,
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od_type="Iter",
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od_wait=200
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)
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print("\nНачало обучения...")
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model.fit(train_pool, eval_set=test_pool, use_best_model=True)
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# --- Оценка качества ---
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# Лучшие метрики по мнению CatBoost
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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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# Перепроверим AUC напрямую
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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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# Порядок фичей
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pd.DataFrame(feature_cols, columns=["feature"]).to_csv(
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"artifacts/feature_order_db.csv", index=False
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)
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print("Порядок фичей сохранен в artifacts/feature_order_db.csv")
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# Важность признаков
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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 25):")
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print(importance_df.head(25).to_string(index=False))
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# При желании — сохранить важности целиком
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importance_df.to_csv("artifacts/feature_importance_db.csv", index=False)
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