Computes balanced scaled f-scores
		Parameters
		----------
		cat_word_counts : np.array
			category counts
		not_cat_word_counts : np.array
			not category counts
		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
			Harmonic means of scaled P(word|category)
			 and scaled P(category|word) for >median half of scores.  Low scores are harmonic means
			 of scaled P(word|~category) and scaled P(~category|word).  Array is squashed to between
			 0 and 1, with 0.5 indicating a median score.

def get_scores(cat_word_counts, not_cat_word_counts,
	               scaler_algo=DEFAULT_SCALER_ALGO, beta=DEFAULT_BETA):
		''' Computes balanced scaled f-scores
		Parameters
		----------
		cat_word_counts : np.array
			category counts
		not_cat_word_counts : np.array
			not category counts
		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
			Harmonic means of scaled P(word|category)
			 and scaled P(category|word) for >median half of scores.  Low scores are harmonic means
			 of scaled P(word|~category) and scaled P(~category|word).  Array is squashed to between
			 0 and 1, with 0.5 indicating a median score.
		'''

		cat_scores = ScaledFScore.get_scores_for_category(cat_word_counts,
		                                                  not_cat_word_counts,
		                                                  scaler_algo,
		                                                  beta)
		not_cat_scores = ScaledFScore.get_scores_for_category(not_cat_word_counts,
		                                                      cat_word_counts,
		                                                      scaler_algo, beta)
		return ScoreBalancer.balance_scores(cat_scores, not_cat_scores)