import nagisa
import pandas as pd
from sklearn.metrics import confusion_matrix
def load_file(filename):
X = []
Y = []
words = []
tags = []
with open(filename, "r") as f:
for line in f:
line = line.rstrip()
if line == "EOS":
assert(len(words) == len(tags))
X.append(words)
Y.append(tags)
words = []
tags = []
else:
line = line.split("\t")
word = " ".join(line[:-1])
tag = line[-1]
words.append(word)
tags.append(tag)
return X, Y
def create_confusion_matrix(tagger, X, Y):
true_cm = []
pred_cm = []
label2id = {}
for i in range(len(X)):
words = X[i]
true_tags = Y[i]
pred_tags = tagger.decode(words) # decoding
if true_tags != pred_tags:
for true_tag, pred_tag in zip(true_tags, pred_tags):
if true_tag != pred_tag:
if true_tag not in label2id:
label2id[true_tag] = len(label2id)
if pred_tag not in label2id:
label2id[pred_tag] = len(label2id)
true_cm.append(label2id[true_tag])
pred_cm.append(label2id[pred_tag])
cm = confusion_matrix(true_cm, pred_cm)
labels = list(label2id.keys())
cm_labeled = pd.DataFrame(cm, columns=labels, index=labels)
return cm_labeled
if __name__ == "__main__":
# load the testset
test_X, test_Y = load_file("ja_gsd_ud.test")
# build the tagger for UD
ud_tagger = nagisa.Tagger(vocabs='ja_gsd_ud.vocabs',
params='ja_gsd_ud.params',
hp='ja_gsd_ud.hp')
# create a confusion matrix if tagger make a mistake in prediction.
cm_labeled = create_confusion_matrix(ud_tagger, test_X, test_Y)
print(cm_labeled)
