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| from abc import ABC, abstractmethod | |
| import sys | |
| import os | |
| from ..analysis.elo import process_fights_for_elo, INITIAL_ELO | |
| import pandas as pd | |
| from sklearn.linear_model import LogisticRegression | |
| from ..config import FIGHTERS_CSV_PATH | |
| from .preprocess import preprocess_for_ml, _get_fighter_history_stats, _calculate_age | |
| class BaseModel(ABC): | |
| """ | |
| Abstract base class for all prediction models. | |
| Ensures that every model has a standard interface for training and prediction. | |
| """ | |
| def train(self, train_fights): | |
| """ | |
| Trains or prepares the model using historical fight data. | |
| :param train_fights: A list of historical fight data dictionaries. | |
| """ | |
| pass | |
| def predict(self, fight): | |
| """ | |
| Predicts the winner of a single fight. | |
| :param fight: A dictionary representing a single fight. | |
| :return: The name of the predicted winning fighter. | |
| """ | |
| pass | |
| class EloBaselineModel(BaseModel): | |
| """ | |
| A baseline prediction model that predicts the winner based on the higher ELO rating. | |
| """ | |
| def __init__(self): | |
| self.fighters_df = None | |
| def train(self, train_fights): | |
| """ | |
| For the ELO baseline, 'training' simply consists of loading the fighter data | |
| to access their ELO scores during prediction. | |
| """ | |
| print("Training EloBaselineModel: Loading fighter ELO data...") | |
| self.fighters_df = pd.read_csv(FIGHTERS_CSV_PATH) | |
| self.fighters_df['full_name'] = self.fighters_df['first_name'] + ' ' + self.fighters_df['last_name'] | |
| self.fighters_df = self.fighters_df.drop_duplicates(subset=['full_name']).set_index('full_name') | |
| def predict(self, fight): | |
| """Predicts the winner based on who has the higher ELO score.""" | |
| f1_name, f2_name = fight['fighter_1'], fight['fighter_2'] | |
| try: | |
| f1_elo = self.fighters_df.loc[f1_name, 'elo'] | |
| f2_elo = self.fighters_df.loc[f2_name, 'elo'] | |
| return f1_name if f1_elo > f2_elo else f2_name | |
| except KeyError as e: | |
| # If a fighter isn't found, we can't make a prediction. | |
| # Returning None or a default is a design choice. | |
| print(f"Warning: Could not find ELO for fighter {e}. Skipping prediction.") | |
| return None | |
| class LogisticRegressionModel(BaseModel): | |
| """ | |
| A model that uses logistic regression to predict fight outcomes based on differential features. | |
| """ | |
| def __init__(self): | |
| self.model = LogisticRegression(solver='liblinear', random_state=42) | |
| self.fighters_df = None | |
| self.fighter_histories = {} | |
| def train(self, train_fights): | |
| """ | |
| Trains the logistic regression model by preprocessing the training data | |
| and fitting the model. | |
| """ | |
| print("Training LogisticRegressionModel...") | |
| # 1. Prepare data for prediction-time feature generation | |
| self.fighters_df = pd.read_csv(FIGHTERS_CSV_PATH) | |
| self.fighters_df['full_name'] = self.fighters_df['first_name'] + ' ' + self.fighters_df['last_name'] | |
| self.fighters_df = self.fighters_df.drop_duplicates(subset=['full_name']).set_index('full_name') | |
| for col in ['height_cm', 'reach_in', 'elo']: | |
| if col in self.fighters_df.columns: | |
| self.fighters_df[col] = pd.to_numeric(self.fighters_df[col], errors='coerce') | |
| # 2. Pre-calculate fighter histories for efficient lookup during prediction | |
| train_fights_with_dates = [] | |
| for fight in train_fights: | |
| fight['date_obj'] = pd.to_datetime(fight['event_date']) | |
| train_fights_with_dates.append(fight) | |
| for fighter_name in self.fighters_df.index: | |
| history = [f for f in train_fights_with_dates if fighter_name in (f['fighter_1'], f['fighter_2'])] | |
| self.fighter_histories[fighter_name] = sorted(history, key=lambda x: x['date_obj']) | |
| # 3. Preprocess training data and fit the model | |
| X_train, y_train, _ = preprocess_for_ml(train_fights, FIGHTERS_CSV_PATH) | |
| print(f"Fitting model on {X_train.shape[0]} samples...") | |
| self.model.fit(X_train, y_train) | |
| print("Model training complete.") | |
| def predict(self, fight): | |
| """ | |
| Predicts the outcome of a single fight by generating its feature vector. | |
| """ | |
| f1_name, f2_name = fight['fighter_1'], fight['fighter_2'] | |
| fight_date = pd.to_datetime(fight['event_date']) | |
| if f1_name not in self.fighters_df.index or f2_name not in self.fighters_df.index: | |
| print(f"Warning: Fighter not found in data. Skipping prediction for {f1_name} vs {f2_name}") | |
| return None | |
| # 1. Get base stats | |
| f1_stats, f2_stats = self.fighters_df.loc[f1_name], self.fighters_df.loc[f2_name] | |
| if isinstance(f1_stats, pd.DataFrame): f1_stats = f1_stats.iloc[0] | |
| if isinstance(f2_stats, pd.DataFrame): f2_stats = f2_stats.iloc[0] | |
| # 2. Get historical stats | |
| f1_hist_stats = _get_fighter_history_stats(f1_name, fight_date, self.fighter_histories.get(f1_name, []), self.fighters_df) | |
| f2_hist_stats = _get_fighter_history_stats(f2_name, fight_date, self.fighter_histories.get(f2_name, []), self.fighters_df) | |
| # 3. Create differential features | |
| f1_age = _calculate_age(f1_stats.get('dob'), fight['event_date']) | |
| f2_age = _calculate_age(f2_stats.get('dob'), fight['event_date']) | |
| features = { | |
| 'elo_diff': f1_stats.get('elo', 1500) - f2_stats.get('elo', 1500), | |
| 'height_diff_cm': f1_stats.get('height_cm', 0) - f2_stats.get('height_cm', 0), | |
| 'reach_diff_in': f1_stats.get('reach_in', 0) - f2_stats.get('reach_in', 0), | |
| 'age_diff_years': (f1_age - f2_age) if f1_age and f2_age else 0, | |
| 'stance_is_different': 1 if f1_stats.get('stance') != f2_stats.get('stance') else 0, | |
| 'wins_last_5_diff': f1_hist_stats['wins_last_n'] - f2_hist_stats['wins_last_n'], | |
| 'avg_opp_elo_last_5_diff': f1_hist_stats['avg_opp_elo_last_n'] - f2_hist_stats['avg_opp_elo_last_n'], | |
| 'ko_percent_last_5_diff': f1_hist_stats['ko_percent_last_n'] - f2_hist_stats['ko_percent_last_n'], | |
| 'sig_str_landed_per_min_last_5_diff': f1_hist_stats['sig_str_landed_per_min_last_n'] - f2_hist_stats['sig_str_landed_per_min_last_n'], | |
| } | |
| feature_vector = pd.DataFrame([features]).fillna(0) | |
| # 4. Predict | |
| # The model predicts the probability of class '1' (a win for fighter_1) | |
| prediction = self.model.predict(feature_vector)[0] | |
| return f1_name if prediction == 1 else f2_name |