65 lines
1.3 KiB
Python
65 lines
1.3 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf-8
|
|
|
|
|
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
|
from sklearn.cluster import KMeans
|
|
import string
|
|
from stop_words import get_stop_words
|
|
|
|
stop_words = get_stop_words('polish')
|
|
|
|
|
|
|
|
inp1 = open('dev-0/in.tsv', 'r', encoding="utf-8")
|
|
out1 = open("dev-0/out.tsv", "w")
|
|
|
|
linia1 = inp1.readlines()
|
|
inp1.close()
|
|
|
|
prep=[]
|
|
for x in linia1:
|
|
temp = ""
|
|
for y in x.split():
|
|
y = y.strip().replace(",", "")
|
|
if y not in stop_words:
|
|
temp = temp + " " + y
|
|
prep.append(temp)
|
|
|
|
vectorizer1 = TfidfVectorizer()
|
|
vectorizer1 = vectorizer1.fit_transform(prep)
|
|
|
|
predict1 = KMeans(n_clusters=25, max_iter=1000).fit_predict(vectorizer1)
|
|
print(predict1)
|
|
|
|
for x in predict1:
|
|
out1.write(str(x) + '\n')
|
|
out1.close()
|
|
|
|
|
|
|
|
inp2 = open('test-A/in.tsv', 'r', encoding="utf-8")
|
|
out2 = open("test-A/out.tsv", "w")
|
|
|
|
linia2 = inp2.readlines()
|
|
inp2.close()
|
|
|
|
prep2=[]
|
|
for x2 in linia1:
|
|
temp2 = ""
|
|
for y2 in x2.split():
|
|
y2 = y2.strip().replace(",", "")
|
|
if y2 not in stop_words:
|
|
temp2 = temp2 + " " + y2
|
|
prep2.append(temp2)
|
|
|
|
vectorizer2 = TfidfVectorizer()
|
|
vectorizer2 = vectorizer2.fit_transform(prep)
|
|
|
|
predict2 = KMeans(n_clusters=25, max_iter=1000).fit_predict(vectorizer2)
|
|
print(predict2)
|
|
|
|
for y in predict2:
|
|
out2.write(str(y) + '\n')
|
|
out2.close()
|