def get_scores_for_category(self): cat_counts, not_cat_counts = self._get_counts() scores = ScaledFScore.get_scores_for_category(cat_counts, not_cat_counts) np.testing.assert_almost_equal(scores, [0.23991183969723384, 0.24969810634506373, 0.23991183969723384, 0.27646711056272855, 0.92885244834997516, 0.42010144843632563, 0.49166017105966719, 0.0, 0.0, 0.50262304057984664])
def get_scores_for_category(self): cat_counts, not_cat_counts = self._get_counts() scores = ScaledFScore.get_scores_for_category(cat_counts, not_cat_counts) np.testing.assert_almost_equal(scores, [ 0.23991183969723384, 0.24969810634506373, 0.23991183969723384, 0.27646711056272855, 0.92885244834997516, 0.42010144843632563, 0.49166017105966719, 0.0, 0.0, 0.50262304057984664 ])
def get_scaled_f_scores_vs_background(self, scaler_algo='none', beta=1.): ''' Parameters ---------- scaler_algo : str see get_scaled_f_scores, default 'none' beta : float default 1. Returns ------- pd.DataFrame of scaled_f_score scores compared to background corpus ''' df = self.get_term_and_background_counts() df['Scaled f-score'] = ScaledFScore.get_scores_for_category( df['corpus'], df['background'], scaler_algo, beta) return df.sort_values(by='Scaled f-score', ascending=False)