Computes unbalanced 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 normcdf
		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))). Defaults to 1.
		Returns
		-------
			np.array of harmonic means of scaled P(word|category) and scaled P(category|word).

def get_scores_for_category(cat_word_counts, not_cat_word_counts,
	                            scaler_algo=DEFAULT_SCALER_ALGO, beta=DEFAULT_BETA):
		''' Computes unbalanced 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 normcdf
		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))). Defaults to 1.
		Returns
		-------
			np.array of harmonic means of scaled P(word|category) and scaled P(category|word).
		'''
		assert beta > 0
		precision = (cat_word_counts * 1. / (cat_word_counts + not_cat_word_counts))
		recall = cat_word_counts * 1. / cat_word_counts.sum()
		precision_normcdf = ScaledFScore._safe_scaler(scaler_algo, precision)
		recall_normcdf = ScaledFScore._safe_scaler(scaler_algo, recall)
		scores = (1 + beta ** 2) * (precision_normcdf * recall_normcdf) \
		         / ((beta ** 2) * precision_normcdf + recall_normcdf)
		scores[np.isnan(scores)] = 0.
		return scores