input: Write a python function to calculate the Mean Absolute Percent Error (MAPE) of a linear regression model's predictions on stock data.
  assert calculate_mape('aapl_stock_data.csv') < 10
  assert isinstance(calculate_mape('aapl_stock_data.csv'), float)
output: import pandas as pd
  from sklearn.linear_model import LinearRegression
  from sklearn.metrics import mean_absolute_percentage_error
  
  def calculate_mape(file_path):
      # Load the stock data
      data = pd.read_csv(file_path)
      
      # Assume the file has columns 'Date', 'Open', 'High', 'Low', 'Close', 'Volume'
      # Let's use 'Open', 'High', 'Low', 'Volume' to predict 'Close'
      features = ['Open', 'High', 'Low', 'Volume']
      target = 'Close'
      
      # Prepare the data
      X = data[features]
      y = data[target]
      
      # Split the data into training and test sets
      train_size = int(0.8 * len(data))
      X_train, X_test = X[:train_size], X[train_size:]
      y_train, y_test = y[:train_size], y[train_size:]
      
      # Train the linear regression model
      model = LinearRegression()
      model.fit(X_train, y_train)
      
      # Make predictions
      y_pred = model.predict(X_test)
      
      # Calculate MAPE
      mape = mean_absolute_percentage_error(y_test, y_pred) * 100
      
      return mape