solution

run.py

@@ -0,0 +1,54 @@
+import vowpalwabbit
+import pandas as pd
+import re
+import sys
+
+file_to_predict = sys.argv[1]
+
+
+def to_vw_format(row):
+    mapping = {'business': 1, 'culture': 2, 'lifestyle': 3, 'news': 4, 'opinion': 5, 'removed': 6, 'sport': 7}
+    try:
+        res = f'{mapping[row.category]} |'
+        rows = row.drop(['category']).iteritems()
+    except:
+        res = '0 |'
+        rows = row.iteritems()
+    for idx, value in rows:
+        if idx == 'text':
+            value = value.lower()
+            value = re.sub("[^a-zA-Z '-]", '', value)
+            value = re.sub("( ')|(' )|('$)|(^')", ' ', value)
+            value = value.strip()
+            value = re.sub(' +', ' ', value)
+        res += f" {idx}:{value}"
+    return res
+
+
+in_df = pd.read_csv('/home/ked/Desktop/aitech/eks/ireland-news-headlines/train/in.tsv', sep='\t', header=None)
+exp_df = pd.read_csv('/home/ked/Desktop/aitech/eks/ireland-news-headlines/train/expected.tsv', sep='\t', header=None)
+pred_df = pd.read_csv(file_to_predict, sep='\t', header=None)
+
+in_df[3] = exp_df[0]
+in_df = in_df.drop(1, 1)
+in_df.columns = ['year', 'text', 'category']
+
+pred_df[3] = exp_df[0] # Leniwe rozwiązanie, żeby funkcja się nie sypała
+pred_df = pred_df.drop(1, 1)
+pred_df.columns = ['year', 'text', 'category']
+
+vw = vowpalwabbit.Workspace('--oaa 7')
+
+for example in in_df.apply(to_vw_format, axis=1):
+    vw.learn(example)
+
+predictions = []
+for example in pred_df.apply(to_vw_format, axis=1):
+    predicted_class = vw.predict(example)
+    predictions.append(predicted_class)
+
+mapping = {1: 'business', 2: 'culture', 3: 'lifestyle', 4: 'news', 5: 'opinion', 6: 'removed', 7: 'sport'}
+
+with open('out.tsv', 'w', encoding='utf-8') as f:
+    for prediction in predictions:
+        f.write(mapping[prediction] + '\n')