predictV1/educationML/train_model_bag_of_heroes.py

import os
import pandas as pd
import numpy as np
from catboost import CatBoostClassifier, Pool
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score

print("Загрузка датасета...")

df = pd.read_parquet("data/dataset_from_db.parquet")

print(f"Всего записей: {len(df)}")
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}%)")

# --- Bag-of-Heroes подход ---
# Создаем бинарные признаки для каждого героя в каждой команде

# Получаем все уникальные ID героев из данных
hero_cols_r = [f"r_h{i}" for i in range(1, 6)]
hero_cols_d = [f"d_h{i}" for i in range(1, 6)]

all_hero_ids = set()
for col in hero_cols_r + hero_cols_d:
    all_hero_ids.update(df[col].dropna().unique())

all_hero_ids = sorted([int(h) for h in all_hero_ids if h >= 0])
print(f"\nВсего уникальных героев: {len(all_hero_ids)}")

# Создаем новый датафрейм с bag-of-heroes признаками
X = pd.DataFrame()

# Добавляем is_first_pick_radiant
X["is_first_pick_radiant"] = df["is_first_pick_radiant"].astype(int)

# Для каждого героя создаем 2 признака: radiant_hero_{id} и dire_hero_{id}
for hero_id in all_hero_ids:
    # Radiant team
    X[f"radiant_hero_{hero_id}"] = 0
    for col in hero_cols_r:
        X.loc[df[col] == hero_id, f"radiant_hero_{hero_id}"] = 1

    # Dire team
    X[f"dire_hero_{hero_id}"] = 0
    for col in hero_cols_d:
        X.loc[df[col] == hero_id, f"dire_hero_{hero_id}"] = 1

print(f"Количество признаков: {len(X.columns)}")
print(f"  - is_first_pick_radiant: 1")
print(f"  - radiant_hero_*: {len(all_hero_ids)}")
print(f"  - dire_hero_*: {len(all_hero_ids)}")

# Целевая переменная
y = df["y"].astype(int).copy()

# Разбиение
X_train, X_test, y_train, y_test = train_test_split(
    X, y,
    test_size=0.2,
    random_state=42,
    stratify=y
)

print(f"\nTrain: {len(X_train)} записей")
print(f"Test:  {len(X_test)} записей")

# В bag-of-heroes все признаки числовые (0 или 1), категориальных нет
train_pool = Pool(X_train, y_train)
test_pool  = Pool(X_test,  y_test)

# Модель
model = CatBoostClassifier(
    iterations=2500,
    learning_rate=0.03,
    depth=7,
    l2_leaf_reg=2,
    bootstrap_type="Bayesian",
    bagging_temperature=1.0,
    loss_function="Logloss",
    eval_metric="AUC",
    random_seed=42,
    verbose=100,
    od_type="Iter",
    od_wait=200
)

print("\nНачало обучения...")
model.fit(train_pool, eval_set=test_pool, use_best_model=True)

# --- Оценка качества ---
best_scores = model.get_best_score()
train_auc_cb = best_scores.get("learn", {}).get("AUC", np.nan)
test_auc_cb  = best_scores.get("validation", {}).get("AUC", np.nan)

y_train_proba = model.predict_proba(train_pool)[:, 1]
y_test_proba  = model.predict_proba(test_pool)[:, 1]
train_auc = roc_auc_score(y_train, y_train_proba)
test_auc  = roc_auc_score(y_test,  y_test_proba)

print(f"\nCatBoost best AUC (learn/valid): {train_auc_cb:.4f} / {test_auc_cb:.4f}")
print(f"Recomputed AUC (train/test):      {train_auc:.4f} / {test_auc:.4f}")

# --- Сохранение ---
os.makedirs("artifacts", exist_ok=True)
model_path = "artifacts/model_bag_of_heroes.cbm"
model.save_model(model_path)
print(f"\nМодель сохранена: {model_path}")

# Порядок фичей
feature_cols = list(X.columns)
pd.DataFrame(feature_cols, columns=["feature"]).to_csv(
    "artifacts/feature_order_bag_of_heroes.csv", index=False
)
print("Порядок фичей сохранен в artifacts/feature_order_bag_of_heroes.csv")

# Важность признаков (топ-30)
importance = model.get_feature_importance(train_pool)
importance_df = (
    pd.DataFrame({"feature": X_train.columns, "importance": importance})
    .sort_values("importance", ascending=False)
    .reset_index(drop=True)
)

print("\nВажность признаков (top 30):")
print(importance_df.head(30).to_string(index=False))

importance_df.to_csv("artifacts/feature_importance_bag_of_heroes.csv", index=False)