def test_get_scores(self):
cat_counts, not_cat_counts = self._get_counts()
scores = ScaledFScore.get_scores(cat_counts, not_cat_counts, beta=1.)
np.testing.assert_almost_equal(
scores,
np.array([
0.2689108, 0., 0.2689108, 0.1266617, 1., 0.5, 0.5590517, 0.5,
0.5, 0.5720015
]))
def _get_scaled_f_score_from_counts(self, cat_word_counts, not_cat_word_counts, scaler_algo,
beta=DEFAULT_BETA):
'''
scaler = self._get_scaler_function(scaler_algo)
p_word_given_category = cat_word_counts.astype(np.float64) / cat_word_counts.sum()
p_category_given_word = cat_word_counts.astype(np.float64) / (cat_word_counts + not_cat_word_counts)
scores \
= self._computer_harmoic_mean_of_probabilities_over_non_zero_in_category_count_terms(
cat_word_counts, p_category_given_word, p_word_given_category, scaler
)
'''
return ScaledFScore.get_scores(cat_word_counts, not_cat_word_counts, scaler_algo, beta=beta)
def test_get_scores_zero_median(self):
cat_counts = np.array([0, 0, 0, 0, 0, 0, 1, 2])
not_cat_counts = np.array([1, 1, 2, 1, 1, 1, 1, 3])
ScaledFScore.get_scores(cat_counts, not_cat_counts)
def test_get_scores_zero_all_same(self):
cat_counts = np.array([0, 0, 0, 0, 0, 0, 1, 2])
not_cat_counts = np.array([1, 1, 2, 1, 1, 1, 1, 2])
scores = ScaledFScore.get_scores(cat_counts, not_cat_counts)
np.testing.assert_almost_equal(scores,
[0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 1.])
def test_get_scores(self):
cat_counts, not_cat_counts = self._get_counts()
scores = ScaledFScore.get_scores(cat_counts, not_cat_counts, beta=1.)
np.testing.assert_almost_equal(scores,
np.array([0.2689108, 0., 0.2689108, 0.1266617, 1.,
0.5, 0.5590517, 0.5, 0.5, 0.5720015]))
def test_get_scores_zero_median(self):
cat_counts = np.array([0, 0, 0, 0, 0, 0, 1, 2])
not_cat_counts = np.array([1, 1, 2, 1, 1, 1, 1, 3])
ScaledFScore.get_scores(cat_counts, not_cat_counts)
def test_get_scores_zero_all_same(self):
cat_counts = np.array([0, 0, 0, 0, 0, 0, 1, 2])
not_cat_counts = np.array([1, 1, 2, 1, 1, 1, 1, 2])
scores = ScaledFScore.get_scores(cat_counts, not_cat_counts)
np.testing.assert_almost_equal(scores, [0.5, 0.5, 0, 0.5, 0.5, 0.5, 0.5, 1.])