First solution

run.py

@@ -0,0 +1,32 @@
+import pandas as pd
+import numpy as np
+from sklearn.naive_bayes import GaussianNB
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from sklearn.metrics import accuracy_score
+
+
+# Pliki zostały wygenerowane poza repozytorium, dlatego są inne ścieżki i nazwy plików
+df = pd.read_csv("train.tsv", sep="\t", header=None)
+dev_X = pd.read_csv("in.tsv", sep="\t", header=None)
+test_X = pd.read_csv("test_in.tsv", sep="\t", header=None)
+
+df = df.head(1500)
+vectorizer = CountVectorizer()
+X = vectorizer.fit_transform(df[1])
+y = df[0]
+model = GaussianNB()
+model.fit(X.toarray(), y)
+
+data_for_dev = model.predict(vectorizer.transform(dev_X[0]).toarray())
+data_for_dev = data_for_dev.tolist()
+data_for_dev = [str(x)+'\n' for x in data_for_dev]
+
+data_for_test = model.predict(vectorizer.transform(test_X[0]).toarray())
+data_for_test = data_for_test.tolist()
+data_for_test = [str(x)+'\n' for x in data_for_test]
+
+with open("out.tsv", "w", encoding="UTF-8") as output_file:
+    output_file.writelines(data_for_dev)
+
+with open("test_out.tsv", "w", encoding="UTF-8") as output_file:
+    output_file.writelines(data_for_test)