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CatBoost Model Wrapper
Standardized wrapper for CatBoost football prediction.
Part of the complete blueprint implementation.
"""
import numpy as np
import pandas as pd
from typing import Dict, List, Optional
from pathlib import Path
import logging
logger = logging.getLogger(__name__)
try:
from catboost import CatBoostClassifier, CatBoostRegressor
CAT_AVAILABLE = True
except ImportError:
CAT_AVAILABLE = False
class CatBoostModel:
"""
CatBoost model wrapper for football predictions.
Supports:
- Native categorical feature handling
- GPU training
- Ordered boosting
"""
DEFAULT_PARAMS = {
'learning_rate': 0.05,
'depth': 6,
'iterations': 200,
'random_seed': 42,
'verbose': False,
'loss_function': 'MultiClass'
}
def __init__(
self,
params: Dict = None,
model_dir: str = "models/saved_models/catboost"
):
self.params = {**self.DEFAULT_PARAMS, **(params or {})}
self.model_dir = Path(model_dir)
self.model_dir.mkdir(parents=True, exist_ok=True)
self.result_model = None
self.goals_model = None
self.btts_model = None
self.feature_names: List[str] = []
self.is_fitted = False
def fit(
self,
X: pd.DataFrame,
y_result: pd.Series,
y_home_goals: pd.Series = None,
y_away_goals: pd.Series = None,
y_btts: pd.Series = None,
cat_features: List[str] = None
) -> 'CatBoostModel':
"""Fit all prediction models."""
if not CAT_AVAILABLE:
logger.error("CatBoost not installed")
return self
self.feature_names = X.columns.tolist()
cat_idx = [X.columns.get_loc(c) for c in (cat_features or []) if c in X.columns]
# Result model
logger.info("Training CatBoost result model...")
self.result_model = CatBoostClassifier(**self.params)
self.result_model.fit(X, y_result, cat_features=cat_idx if cat_idx else None)
# Goals models
if y_home_goals is not None:
logger.info("Training CatBoost goals model...")
goals_params = {**self.params}
goals_params['loss_function'] = 'RMSE'
self.goals_model = {
'home': CatBoostRegressor(**goals_params),
'away': CatBoostRegressor(**goals_params)
}
self.goals_model['home'].fit(X, y_home_goals, cat_features=cat_idx if cat_idx else None)
if y_away_goals is not None:
self.goals_model['away'].fit(X, y_away_goals, cat_features=cat_idx if cat_idx else None)
# BTTS model
if y_btts is not None:
logger.info("Training CatBoost BTTS model...")
btts_params = {**self.params}
btts_params['loss_function'] = 'Logloss'
self.btts_model = CatBoostClassifier(**btts_params)
self.btts_model.fit(X, y_btts, cat_features=cat_idx if cat_idx else None)
self.is_fitted = True
logger.info("CatBoost training complete")
return self
def predict(self, X: pd.DataFrame) -> Dict:
"""Make predictions."""
if not self.is_fitted:
return {}
predictions = {}
if self.result_model:
probs = self.result_model.predict_proba(X)
predictions['1x2'] = {
'home': float(probs[0, 0]),
'draw': float(probs[0, 1]),
'away': float(probs[0, 2])
}
predictions['result'] = ['H', 'D', 'A'][np.argmax(probs[0])]
if self.goals_model:
predictions['home_goals'] = float(self.goals_model['home'].predict(X)[0])
predictions['away_goals'] = float(self.goals_model['away'].predict(X)[0])
total = predictions['home_goals'] + predictions['away_goals']
predictions['over_2.5'] = 1 / (1 + np.exp(-(total - 2.5)))
if self.btts_model:
btts_prob = self.btts_model.predict_proba(X)
predictions['btts'] = float(btts_prob[0, 1]) if btts_prob.shape[1] > 1 else 0.5
return predictions
def get_feature_importance(self, top_n: int = 20) -> pd.DataFrame:
"""Get feature importance."""
if not self.result_model:
return pd.DataFrame()
importance = self.result_model.feature_importances_
return pd.DataFrame({
'feature': self.feature_names,
'importance': importance
}).sort_values('importance', ascending=False).head(top_n)
def save(self, name: str = "catboost_model"):
"""Save models."""
if self.result_model:
self.result_model.save_model(str(self.model_dir / f"{name}_result.cbm"))
if self.goals_model:
self.goals_model['home'].save_model(str(self.model_dir / f"{name}_home_goals.cbm"))
self.goals_model['away'].save_model(str(self.model_dir / f"{name}_away_goals.cbm"))
if self.btts_model:
self.btts_model.save_model(str(self.model_dir / f"{name}_btts.cbm"))
def load(self, name: str = "catboost_model") -> bool:
"""Load models."""
try:
result_path = self.model_dir / f"{name}_result.cbm"
if result_path.exists():
self.result_model = CatBoostClassifier()
self.result_model.load_model(str(result_path))
home_path = self.model_dir / f"{name}_home_goals.cbm"
away_path = self.model_dir / f"{name}_away_goals.cbm"
if home_path.exists():
self.goals_model = {
'home': CatBoostRegressor(),
'away': CatBoostRegressor()
}
self.goals_model['home'].load_model(str(home_path))
if away_path.exists():
self.goals_model['away'].load_model(str(away_path))
btts_path = self.model_dir / f"{name}_btts.cbm"
if btts_path.exists():
self.btts_model = CatBoostClassifier()
self.btts_model.load_model(str(btts_path))
self.is_fitted = self.result_model is not None
return True
except Exception as e:
logger.error(f"Load failed: {e}")
return False
_model: Optional[CatBoostModel] = None
def get_model() -> CatBoostModel:
global _model
if _model is None:
_model = CatBoostModel()
return _model
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