Computes scaled-fscores
        Parameters
        ----------
        category : str
            category name to score
        scaler_algo : str
          Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
        beta : float
            Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Scale(P(c|w))). Default.
        Returns
        -------
            np.array of harmonic means of scaled P(word|category) and scaled P(category|word)

def get_scaled_f_scores(self,
                            category,
                            scaler_algo=DEFAULT_SCALER_ALGO,
                            beta=DEFAULT_BETA):
        ''' Computes scaled-fscores
        Parameters
        ----------
        category : str
            category name to score
        scaler_algo : str
          Function that scales an array to a range \in [0 and 1]. Use 'percentile', 'normcdf'. Default.
        beta : float
            Beta in (1+B^2) * (Scale(P(w|c)) * Scale(P(c|w)))/(B^2*Scale(P(w|c)) + Scale(P(c|w))). Default.
        Returns
        -------
            np.array of harmonic means of scaled P(word|category) and scaled P(category|word)
        '''

        assert beta > 0
        cat_word_counts, not_cat_word_counts = self._get_catetgory_and_non_category_word_counts(category)
        scores = self._get_scaled_f_score_from_counts(cat_word_counts, not_cat_word_counts, scaler_algo, beta)
        return np.array(scores)