from gensim.models.keyedvectors import KeyedVectors
import numpy as np
import numpy.linalg as la
from konlpy.tag import Twitter
import scipy.stats as st
import argparse
testset = "test_dataset/kor_ws353.csv"
twitter = Twitter()
def normalize(array):
norm = la.norm(array)
return array / norm
def create_word_vector(word, pos_vectors):
pos_list = twitter.pos(word, norm=True)
word_vector = np.sum([pos_vectors.word_vec(str(pos).replace(" ", "")) for pos in pos_list], axis=0)
return normalize(word_vector)
def word_sim_test(filename, pos_vectors):
delim = ','
actual_sim_list, pred_sim_list = [], []
missed = 0
with open(filename, 'r') as pairs:
for pair in pairs:
w1, w2, actual_sim = pair.strip().split(delim)
try:
w1_vec = create_word_vector(w1, pos_vectors)
w2_vec = create_word_vector(w2, pos_vectors)
pred = float(np.inner(w1_vec, w2_vec))
actual_sim_list.append(float(actual_sim))
pred_sim_list.append(pred)
except KeyError:
missed += 1
spearman, _ = st.spearmanr(actual_sim_list, pred_sim_list)
pearson, _ = st.pearsonr(actual_sim_list, pred_sim_list)
return spearman, pearson, missed
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("pos_file", type=str, help="trained morpheme vectors file")
args = parser.parse_args()
pos_vectors = KeyedVectors.load_word2vec_format(args.pos_file, binary=False)
spearman, pearson, missed = word_sim_test(testset, pos_vectors)
print("Missing words :", missed)
print("Spearman coefficient :", spearman)
print("Pearson coefficient :", pearson)
