Prześlij pliki do ''

run.py

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