Initial commit: добавление проекта predictV1

Включает модели ML для предсказаний, API маршруты, скрипты обучения и данные.

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

educationML/train_model_pro.py

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+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)")