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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() | |
OU = data['Over'] | |
data.drop(columns=['Home-Team-Win','Over','Season','home_team','away_team','game_date','Key','Home Score','Away Score','Home Odds Close','Away Odds Close','Home Winnings','Away Winnings','Away Odds','Home Odds'], inplace=True) | |
acc_results = [] | |
for x in tqdm(range(100)): | |
X_train, X_test, y_train, y_test = train_test_split(data, OU, 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': 0.05, | |
'objective': 'multi:softprob', | |
'num_class': 3 | |
} | |
epochs = 300 | |
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_OU_no_odds.pkl') | |
with open(file_path,'wb') as f: | |
pkl.dump(train_games,f) | |
file_path = os.path.join(pickle_directory, 'test_games_OU_no_odds.pkl') | |
with open(file_path,'wb') as f: | |
pkl.dump(test_games,f) | |
file_path = os.path.join(model_directory, f'xgboost_OU_no_odds_{acc}%.json') | |
model.save_model(file_path) | |
print('Done') |