def acc_per_tag(self, y_hat, y):
assert isinstance(y_hat, pd.Series)
assert isinstance(y, pd.Series)
roll_y = pd.Series(y.values.reshape(-1)).drop(
['<PAD>', '*', '<STOP>', ','])
roll_y_hat = pd.Series(y_hat.values.reshape(-1)).drop(
['<PAD>', '*', '<STOP>', ','])
most_reacuent_tags = self.tag_corpus[:10]
sc = Score(most_reacuent_tags)
sc.fit(roll_y, roll_y_hat)
return sc.acc_per_tag(roll_y, roll_y_hat)
def test_all_data(self):
data = PreprocessTags().load_data(r'..\data\test.wtag')
y_hat = data.y
y = data.y
roll_y = pd.Series(y.values.reshape(-1))
roll_y_hat = pd.Series(y_hat.values.reshape(-1))
index = pd.value_counts(y.values.reshape(-1)).index
most_reacuent_tags = pd.Series(index,
index=index).drop(['<STOP>', '*'])[:10]
sc = Score(most_reacuent_tags)
sc.fit(roll_y, roll_y_hat)
cm = sc.matrix_confusion()
acc_dict = sc.acc_per_tag(y, y_hat)
print(acc_dict)
print(cm)
