data_processor.py
def _split_datafiles(data, val_size=0.2, test_size=0.2):
        """Spit all the data we have into training, validating, and test sets.

        By default, 60/20/20 split
        Credit: https://www.slideshare.net/TaegyunJeon1/electricity-price-forecasting-with-recurrent-neural-networks

        Args:
            data (list): list of filenames
            val_size (float, optional): Percentage of data to be used for validation set
            test_size (float, optional): Percentage to data set to be used for test set

        Returns:
            train_set (list): list of training example filenames
            val_set (list): list of validation example filenames
            test_set (list): list of test example filenames
        """
        val_length = int(len(data) * val_size)
        test_length = int(len(data) * test_size)

        val_set = data[:val_length]
        test_set = data[val_length:val_length + test_length]
        train_set = data[val_length + test_length:]

        return train_set, val_set, test_set
Spit all the data we have into training, validating, and test sets.

        By default, 60/20/20 split
        Credit: https://www.slideshare.net/TaegyunJeon1/electricity-price-forecasting-with-recurrent-neural-networks

        Args:
            data (list): list of filenames
            val_size (float, optional): Percentage of data to be used for validation set
            test_size (float, optional): Percentage to data set to be used for test set

        Returns:
            train_set (list): list of training example filenames
            val_set (list): list of validation example filenames
            test_set (list): list of test example filenames
        
Spit all the data we have into training, validating, and test sets.
Spit all the data we have into training validating and test sets .
def _split_datafiles(data, val_size=0.2, test_size=0.2):
        val_length = int(len(data) * val_size)
        test_length = int(len(data) * test_size)
        val_set = data[:val_length]
        test_set = data[val_length:val_length + test_length]
        train_set = data[val_length + test_length:]
        return train_set, val_set, test_set
def _split_datafiles ( data , val_size = 0.2 , test_size = 0.2 ) : val_length = int ( len ( data ) * val_size ) test_length = int ( len ( data ) * test_size ) val_set = data [ : val_length ] test_set = data [ val_length : val_length + test_length ] train_set = data [ val_length + test_length : ] return train_set , val_set , test_set
Spit all the data we have into training, validating, and test sets.
Spit all the data we have into training validating and test sets .
"""Spit all the data we have into training, validating, and test sets.

        By default, 60/20/20 split
        Credit: https://www.slideshare.net/TaegyunJeon1/electricity-price-forecasting-with-recurrent-neural-networks

        Args:
            data (list): list of filenames
            val_size (float, optional): Percentage of data to be used for validation set
            test_size (float, optional): Percentage to data set to be used for test set

        Returns:
            train_set (list): list of training example filenames
            val_set (list): list of validation example filenames
            test_set (list): list of test example filenames
        """
None
data
list of filenames
list of filenames
val_size
Percentage of data to be used for validation set
Percentage of data to be used for validation set
test_size
Percentage to data set to be used for test set
Percentage to data set to be used for test set