deepcut/utils.py
def create_n_gram_df(df, n_pad):
    """
    Given input dataframe, create feature dataframe of shifted characters
    """
    n_pad_2 = int((n_pad - 1)/2)
    for i in range(n_pad_2):
        df['char-{}'.format(i+1)] = df['char'].shift(i + 1)
        df['type-{}'.format(i+1)] = df['type'].shift(i + 1)
        df['char{}'.format(i+1)] = df['char'].shift(-i - 1)
        df['type{}'.format(i+1)] = df['type'].shift(-i - 1)
    return df[n_pad_2: -n_pad_2]
def create_n_gram_df(df, n_pad):
    """
    Given input dataframe, create feature dataframe of shifted characters
    """
    n_pad_2 = int((n_pad - 1)/2)
    for i in range(n_pad_2):
        df['char-{}'.format(i+1)] = df['char'].shift(i + 1)
        df['type-{}'.format(i+1)] = df['type'].shift(i + 1)
        df['char{}'.format(i+1)] = df['char'].shift(-i - 1)
        df['type{}'.format(i+1)] = df['type'].shift(-i - 1)
    return df[n_pad_2: -n_pad_2]
def create_n_gram_df ( df , n_pad ) : n_pad_2 = int ( ( n_pad - 1 ) / 2 ) for i in range ( n_pad_2 ) : df [ 'char-{}' . format ( i + 1 ) ] = df [ 'char' ] . shift ( i + 1 ) df [ 'type-{}' . format ( i + 1 ) ] = df [ 'type' ] . shift ( i + 1 ) df [ 'char{}' . format ( i + 1 ) ] = df [ 'char' ] . shift ( - i - 1 ) df [ 'type{}' . format ( i + 1 ) ] = df [ 'type' ] . shift ( - i - 1 ) return df [ n_pad_2 : - n_pad_2 ]
Given input dataframe, create feature dataframe of shifted characters
Given input dataframe create feature dataframe of shifted characters