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| import xgboost as xgb | |
| import pandas as pd | |
| import pickle as pkl | |
| import numpy as np | |
| from tqdm import tqdm | |
| from IPython.display import clear_output | |
| from sklearn.metrics import accuracy_score | |
| from sklearn.model_selection import train_test_split | |
| import os | |
| current_directory = os.path.dirname(os.path.abspath(__file__)) | |
| parent_directory = os.path.dirname(current_directory) | |
| data_directory = os.path.join(parent_directory, 'Data') | |
| model_directory = os.path.join(parent_directory, 'Models') | |
| pickle_directory = os.path.join(parent_directory, 'Pickles') | |
| file_path = os.path.join(data_directory, 'gbg_and_odds.csv') | |
| data = pd.read_csv(file_path).dropna() | |
| margin = data['Home-Team-Cover'] | |
| data.drop(columns=['Home-Team-Win','Home-Team-Cover','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings', 'Home Odds', 'Away Odds'], inplace=True) | |
| features = [i for i in data.columns if i!='game_id'] | |
| print(features) | |
| acc_results = [] | |
| for x in tqdm(range(100)): | |
| X_train, X_test, y_train, y_test = train_test_split(data, margin, test_size=.1) | |
| train_games = X_train['game_id'] | |
| test_games = X_test['game_id'] | |
| X_train.drop(columns=['game_id'], inplace=True) | |
| X_test.drop(columns=['game_id'], inplace=True) | |
| train = xgb.DMatrix(X_train.astype(float).values, label=y_train) | |
| test = xgb.DMatrix(X_test.astype(float).values, label=y_test) | |
| param = { | |
| 'max_depth': 6, | |
| 'eta': 0.01, | |
| 'objective': 'multi:softprob', | |
| 'num_class': 3 | |
| } | |
| epochs = 500 | |
| model = xgb.train(param, train, epochs) | |
| predictions = model.predict(test) | |
| y = [] | |
| for z in predictions: | |
| y.append(np.argmax(z)) | |
| acc = round(accuracy_score(y_test, y)*100, 1) | |
| acc_results.append(acc) | |
| clear_output(wait=True) | |
| print(f"Best accuracy: {max(acc_results)}%") | |
| # only save results if they are the best so far | |
| if acc == max(acc_results): | |
| file_path = os.path.join(pickle_directory, 'train_games_ATS_no_odds.pkl') | |
| with open(file_path,'wb') as f: | |
| pkl.dump(train_games,f) | |
| file_path = os.path.join(pickle_directory, 'test_games_ATS_no_odds.pkl') | |
| with open(file_path,'wb') as f: | |
| pkl.dump(test_games,f) | |
| file_path = os.path.join(model_directory, f'xgboost_ATS_no_odds_{acc}%.json') | |
| model.save_model(file_path) | |
| importances = (model.get_score(importance_type='gain')) | |
| print(pd.DataFrame(zip(features,importances.values())).sort_values(1,ascending=False)) | |
| print('Done') | |