Add script and predictions

run.py

@@ -0,0 +1,33 @@
+import pandas as pd
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+data_raw = pd.read_csv('train/train.tsv', delimiter = '\t', header = None,usecols=[0, 1], names = ['y', 'X'])
+
+import string
+import os
+
+from sklearn.feature_extraction.text import TfidfVectorizer
+from stop_words import get_stop_words
+
+
+def preprocess(item):
+    to_replace = '''~!@#$%^&*()_+-=[]{};\'":?/.>,<1234567890–”’'''
+    for r in to_replace:
+      item = item.replace(r, '')
+    return item.lower()
+
+
+stop_words = get_stop_words('polish') + ['aby', 'tych', 'tym', 'tyle', 'tymi', 'też']
+vectorizer = TfidfVectorizer(stop_words=stop_words, preprocessor=preprocess)
+tfs = vectorizer.fit_transform(data_raw.X)
+
+from sklearn.naive_bayes import MultinomialNB
+clf = MultinomialNB()
+
+clf.fit(tfs,data_raw.y)
+
+for dir in ['dev-0/', 'test-A/']:
+  test_raw = pd.read_csv(dir+'in.tsv', delimiter = '\t', header = None,usecols=[0], names = ['X'])
+  X_test = vectorizer.transform(test_raw.X)
+  y_predicted = clf.predict(X_test)
+  np.savetxt(dir+"out.tsv", y_predicted, delimiter="\t")