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| import pandas as pd | |
| import numpy as np | |
| from tensorflow import keras | |
| from tensorflow.keras import layers | |
| from tensorflow.keras.losses import BinaryCrossentropy | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.model_selection import RandomizedSearchCV | |
| from scikeras.wrappers import KerasClassifier | |
| def create_stats(roster, schedule): | |
| home_stats = [] | |
| away_stats = [] | |
| S = [] | |
| # Loading Relavent Columns from f-test | |
| cols = ['TEAM','PTS/G', 'ORB', 'DRB', 'AST', 'STL', 'BLK', 'TOV', '3P%', 'FT%','2P'] | |
| new_roster = roster[cols] | |
| for i in schedule['Home/Neutral']: | |
| home_stats.append((new_roster[new_roster['TEAM'] == i]).values.tolist()) | |
| for i in schedule['Visitor/Neutral']: | |
| away_stats.append((new_roster.loc[new_roster['TEAM'] == i]).values.tolist()) | |
| for i in range(len(home_stats)): | |
| arr = [] | |
| for j in range(len(home_stats[i])): | |
| del home_stats[i][j][0] | |
| arr += home_stats[i][j] | |
| for j in range(len(away_stats[i])): | |
| del away_stats[i][j][0] | |
| arr += away_stats[i][j] | |
| # Create numpy array with all the players on the Home Team's Stats followed by the Away Team's stats | |
| S.append(np.nan_to_num(np.array(arr), copy=False)) | |
| return S | |
| roster = pd.read_csv('player_stats.txt', delimiter=',') | |
| schedule = pd.read_csv('schedule.txt', delimiter=',') | |
| # Create winning condition to train on | |
| schedule['winner'] = schedule.apply(lambda x: 0 if x['PTS'] > x['PTS.1'] else 1, axis=1) | |
| X = np.array(create_stats(roster, schedule)) | |
| y = np.array(schedule['winner']) | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| def create_model(optimizer='rmsprop', init='glorot_uniform'): | |
| inputs = keras.Input(shape=(100,)) | |
| dense = layers.Dense(50, activation="relu") | |
| x = dense(inputs) | |
| x = layers.Dense(64, activation="relu")(x) | |
| outputs = layers.Dense(1, activation='sigmoid')(x) | |
| model = keras.Model(inputs=inputs, outputs=outputs, name="nba_model") | |
| model.compile(loss=BinaryCrossentropy(from_logits=False), optimizer=optimizer, metrics=["accuracy"]) | |
| return model | |
| model = KerasClassifier(model=create_model, verbose=0, init='glorot_uniform') | |
| optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam'] | |
| init = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'] | |
| epochs = [500, 1000, 1500] | |
| batches = [50, 100, 200] | |
| param_grid = dict(optimizer=optimizer, epochs=epochs, batch_size=batches, init=init) | |
| random_search = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100, verbose=3) | |
| random_search_result = random_search.fit(X_train, y_train) | |
| best_model = random_search_result.best_estimator_ | |
| best_model.model_.save('winner.keras') | |
| best_parameters = random_search_result.best_params_ | |
| print("Best parameters: ", best_parameters) | |
| test_accuracy = random_search_result.best_estimator_.score(X_test, y_test) | |
| print("Test accuracy: ", test_accuracy) | |