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 (матчей): {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}%)")

# --- Создаём признаки на уровне матча ---
print("\nСоздание признаков...")

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)]
pos_cols_r = [f"rp_h{i}" for i in range(1, 6)]
pos_cols_d = [f"dp_h{i}" for i in range(1, 6)]

# Создаём признаки: каждый герой на каждой позиции для каждой команды
# Формат: radiant_{hero_id}_pos_{position}, dire_{hero_id}_pos_{position}

rows = []

for idx, row in df.iterrows():
    features = {}

    # Radiant heroes с позициями
    for i in range(5):
        hero_id = int(row[hero_cols_r[i]])
        position = int(row[pos_cols_r[i]])

        if hero_id >= 0 and position >= 0:
            features[f"radiant_h{hero_id}_p{position}"] = 1

    # Dire heroes с позициями
    for i in range(5):
        hero_id = int(row[hero_cols_d[i]])
        position = int(row[pos_cols_d[i]])

        if hero_id >= 0 and position >= 0:
            features[f"dire_h{hero_id}_p{position}"] = 1

    features['y'] = int(row['y'])
    rows.append(features)

df_features = pd.DataFrame(rows).fillna(0)

print(f"Создано признаков: {len(df_features.columns) - 1}")

# Целевая
y = df_features['y'].astype(int)
X = df_features.drop('y', axis=1)

# 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)} матчей")
print(f"Test:  {len(X_test)} матчей")

# Обучение
train_pool = Pool(X_train, y_train)
test_pool = Pool(X_test, y_test)

model = CatBoostClassifier(
    iterations=1000,
    learning_rate=0.05,
    depth=5,
    l2_leaf_reg=3,
    min_data_in_leaf=10,
    bootstrap_type="Bayesian",
    bagging_temperature=0.5,
    loss_function="Logloss",
    eval_metric="AUC",
    random_seed=42,
    verbose=50,
    od_type="Iter",
    od_wait=100,
    use_best_model=True
)

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

# Оценка
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_from_db_pro_v3.cbm"
model.save_model(model_path)
print(f"\nМодель сохранена: {model_path}")

# Важность (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_db.csv", index=False)

# Сохраняем список всех возможных признаков для инференса
all_features = sorted(X.columns.tolist())
pd.DataFrame(all_features, columns=["feature"]).to_csv(
    "artifacts/feature_order_db.csv", index=False
)
print(f"Порядок фичей сохранен в artifacts/feature_order_db.csv ({len(all_features)} признаков)")