Upload train_model.py

train_model.py

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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)