2.6 KiB
2.6 KiB
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
with open("train/in.tsv") as f:
x_train = f.readlines()
with open("train/expected.tsv") as f:
y_train = f.readlines()
y_train = LabelEncoder().fit_transform(y_train)
y_train
array([1, 0, 0, ..., 0, 0, 1])
pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
model = pipeline.fit(x_train, y_train)
with open("dev-0/in.tsv") as f:
x_dev = f.readlines()
prediction = model.predict(x_dev)
np.savetxt("dev-0/out.tsv", prediction, fmt='%d')
with open("test-A/in.tsv") as f:
x_test = f.readlines()
prediction = model.predict(x_test)
np.savetxt("test-A/out.tsv", prediction, fmt='%d')