NeuralNetworkRandomizeOrder.py

from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas as pd
from scipy.stats import spearmanr
import joblib

Folder = 'DataFiles\\'

year = 2023
start_date = f"{year}-04-01"
end_date = f"{year}-10-01"
final_dataframe = pd.read_pickle(f'{Folder}player_game_stats_{start_date}_to_{end_date}.pkl')

# Remove rows with NaN values
final_dataframe = final_dataframe.dropna()

# Select the relevant features and target variable
features = [
    'Hits_Per_Game_1_games', 'Hits_Per_Game_3_games', 'Hits_Per_Game_7_games', 'Hits_Per_Game_All_games',
    'Hits_Per_PA_1_games', 'Hits_Per_PA_3_games', 'Hits_Per_PA_7_games', 'Hits_Per_PA_All_games',
    '1_Starter', '1_MiddleReliever', '1_EndingPitcher',
    '3_Starter', '3_MiddleReliever', '3_EndingPitcher',
    '7_Starter', '7_MiddleReliever', '7_EndingPitcher',
    'All_Starter', 'All_MiddleReliever', 'All_EndingPitcher',
    'Stadium_Hits'
]
target = 'Hits'

# Create a new DataFrame with the selected features and target
selected_dataframe = final_dataframe[features + [target]]

# Randomize the order of the DataFrame
selected_dataframe = selected_dataframe.sample(frac=1, random_state=42)

X = selected_dataframe[features]
y = selected_dataframe[target]

# Create a scaler for all features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# Create a neural network regressor
nn_model = MLPRegressor(hidden_layer_sizes=(100, 50), activation='relu', solver='adam', max_iter=1000, random_state=42)

# Train the neural network
nn_model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = nn_model.predict(X_test)

# Evaluate the model
mae = mean_absolute_error(y_test, y_pred)
mape = mean_absolute_percentage_error(y_test, y_pred)
spearman_corr, _ = spearmanr(y_pred, y_test)

print(f"\nMean Absolute Error: {mae:.4f}")
print(f"Mean Absolute Percentage Error: {mape:.4f}")
print(f"Spearman Correlation: {spearman_corr:.4f}")

# Save the trained model to a file
joblib.dump(nn_model, 'nn_model.pkl')