sport-text-classification-b.../skrypt-dev-0.py

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2021-05-02 23:16:33 +02:00
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)