39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
![]() |
import numpy as np
|
||
|
from sklearn.naive_bayes import MultinomialNB
|
||
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
||
|
from sklearn import preprocessing
|
||
|
from sklearn.pipeline import make_pipeline
|
||
|
import csv
|
||
|
|
||
|
prep = preprocessing.LabelEncoder()
|
||
|
|
||
|
with open("train/train.tsv") as file_train:
|
||
|
csv_input = csv.reader(file_train, delimiter='\t')
|
||
|
X = []
|
||
|
Y = []
|
||
|
for line in csv_input:
|
||
|
Y.append(line[0])
|
||
|
X.append(line[1])
|
||
|
|
||
|
Y = prep.fit_transform(Y)
|
||
|
with open("dev-0/in.tsv") as file_in:
|
||
|
work_file_lines = file_in.readlines()
|
||
|
MNB = make_pipeline(TfidfVectorizer(use_idf = True), MultinomialNB())
|
||
|
|
||
|
model = MNB.fit(X,Y)
|
||
|
|
||
|
y_predict = model.predict(work_file_lines)
|
||
|
y_predict = np.array(y_predict)
|
||
|
|
||
|
np.set_printoptions(threshold=np.inf)
|
||
|
labels = np.array2string(y_predict.flatten(), separator='\n', suppress_small=True)
|
||
|
|
||
|
file_out = open("dev-0/out.tsv", 'w')
|
||
|
file_out.write(labels[1:-1])
|
||
|
|
||
|
with open("dev-0/out.tsv", 'r') as fix_space:
|
||
|
lines = fix_space.readlines()
|
||
|
|
||
|
lines = [line.replace(' ', '') for line in lines]
|
||
|
with open("dev-0/out.tsv", 'w') as fix_space:
|
||
|
fix_space.writelines(lines)
|