Train Test Validation Split

# split data in 80%/10%/10% train/validation/test sets
valid_set_size_percentage = 10 
test_set_size_percentage = 10 



# function to create train, validation, test data given stock data and sequence length
def load_data(stock, seq_len):
    data_raw = stock.as_matrix() # convert to numpy array
    data = []
    
    # create all possible sequences of length seq_len
    for index in range(len(data_raw) - seq_len): 
        data.append(data_raw[index: index + seq_len])
    
    data = np.array(data);
    valid_set_size = int(np.round(valid_set_size_percentage/100*data.shape[0]));  
    test_set_size = int(np.round(test_set_size_percentage/100*data.shape[0]));
    train_set_size = data.shape[0] - (valid_set_size + test_set_size);
    
    x_train = data[:train_set_size,:-1,:]
    y_train = data[:train_set_size,-1,:]
    
    x_valid = data[train_set_size:train_set_size+valid_set_size,:-1,:]
    y_valid = data[train_set_size:train_set_size+valid_set_size,-1,:]
    
    x_test = data[train_set_size+valid_set_size:,:-1,:]
    y_test = data[train_set_size+valid_set_size:,-1,:]
    
    return [x_train, y_train, x_valid, y_valid, x_test, y_test]