Prześlij pliki do ''

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Joanna Kurczalska 2022-05-11 00:07:10 +02:00
parent 33b253e477
commit da944b72cb

64
run.py
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@ -1,32 +1,32 @@
import lzma
import nltk
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
in_train = [x.strip().lower() for x in train_file.readlines()]
with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
out_train = [int(x.strip()) for x in train_file.readlines()]
with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
in_dev = [x.strip().lower() for x in dev_file.readlines()]
with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
in_test = [x.strip().lower() for x in test_file.readlines()]
tfidf_vectorizer=TfidfVectorizer(stop_words="english")
IN_train = tfidf_vectorizer.fit_transform(in_train)
classifier = MultinomialNB()
y_pred = classifier.fit(IN_train, out_train)
y_prediction = y_pred.predict(tfidf_vectorizer.transform(in_test))
with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
for single_pred in y_prediction:
test_out_file.writelines(f"{str(single_pred)}\n")
pred_dev = y_pred.predict(tfidf_vectorizer.transform(in_test))
with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
for single_pred in pred_dev:
dev_out_file.writelines(f"{str(single_pred)}\n")
import lzma
import nltk
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import TfidfVectorizer
with lzma.open("train/in.tsv.xz", "rt", encoding="utf-8") as train_file:
in_train = [x.strip().lower() for x in train_file.readlines()]
with open("train/expected.tsv", "r", encoding="utf-8") as train_file:
out_train = [int(x.strip()) for x in train_file.readlines()]
with lzma.open("dev-0/in.tsv.xz", "rt", encoding="utf-8") as dev_file:
in_dev = [x.strip().lower() for x in dev_file.readlines()]
with lzma.open("test-A/in.tsv.xz", "rt", encoding="utf-8") as test_file:
in_test = [x.strip().lower() for x in test_file.readlines()]
tfidf_vectorizer=TfidfVectorizer()
IN_train = tfidf_vectorizer.fit_transform(in_train)
classifier = MultinomialNB()
y_pred = classifier.fit(IN_train, out_train)
y_prediction = y_pred.predict(tfidf_vectorizer.transform(in_test))
with open("test-A/out.tsv", "w", encoding="utf-8") as test_out_file:
for single_pred in y_prediction:
test_out_file.writelines(f"{str(single_pred)}\n")
pred_dev = y_pred.predict(tfidf_vectorizer.transform(in_test))
with open("dev-0/out.tsv", "w", encoding="utf-8") as dev_out_file:
for single_pred in pred_dev:
dev_out_file.writelines(f"{str(single_pred)}\n")