test 2 version

model.py

@@ -1,28 +1,56 @@
 import lzma
 from nltk.tokenize import word_tokenize
+from nltk import trigrams
+import string
+from collections import defaultdict, Counter
+
+
+trigrams_list = []
+model = defaultdict(lambda: defaultdict(lambda: 0))
+
+
+def preprocess(text):
+    _text = text.lower().replace('\\n', ' ').strip()
+    for character in _text:
+        if character not in string.ascii_lowercase + ' ':
+            _text = _text.replace(character, '')
+    return word_tokenize(_text)
+
 
 def predict(word_before, word_after):
-    return 'the'
-
-# with open('./dev-0/in.tsv', 'w', encoding='utf-8') as file:
-#     text = lzma.open('./dev-0/in.tsv.xz').read().decode('utf-8')
-#     file.write(text)
-
-# with open('./dev-0/in.tsv', encoding='utf-8') as file_in, open('./dev-0/expected.tsv', encoding='utf-8') as file_expected, open('./dev-0/out.tsv', 'w', encoding='utf-8') as file_out:
-#     for line_in, line_expected in zip(file_in, file_expected):
-#         _, _, _, _, _, _, before, after = line_in.split('\t')
-#         before = word_tokenize(before.replace('\\n', '\n'))
-#         after = word_tokenize(after.replace('\\n', '\n'))
-#         file_out.write(predict(before[-1], after[0]) + '\n')
+    prob_list = list(model[(word_before, word_after)].items())
+    print(prob_list)
+    max_prob = max(prob_list, key=lambda pair: pair[1], default=('', 0.0))
+    if max_prob[1] == 0:
+        return 'the'
+    return max_prob[0]
 
 
-with open('./test-A/in.tsv', 'w', encoding='utf-8') as file:
-    text = lzma.open('./test-A/in.tsv.xz').read().decode('utf-8')
+with open('train/in.tsv', 'w', encoding='utf-8') as file:
+    text = lzma.open('train/in.tsv.xz').read().decode('utf-8')
     file.write(text)
 
-with open('./test-A/in.tsv', encoding='utf-8') as file_in, open('./test-A/out.tsv', 'w', encoding='utf-8') as file_out:
+with open('train/in.tsv', encoding='utf-8') as file_in, open('train/expected.tsv', encoding='utf-8') as file_expected:
+    for index, (line_in, expected) in enumerate(zip(file_in, file_expected)):
+        if index % 1000 == 0:
+            print(index)
+        _, _, _, _, _, _, before, after = line_in.split('\t')
+        before, expected, after = preprocess(before), preprocess(expected), preprocess(after)
+        words = before + expected + after
+        trigrams_list += trigrams(words, pad_right=True, pad_left=True)
+
+for trigram in trigrams_list:
+    if not trigram[0] or not trigram[1] or not trigram[2]:
+        continue
+    model[(trigram[0], trigram[2])][trigram[1]] += 1
+
+for words_1_3 in model:
+    count = sum(model[words_1_3].values())
+    for word_2 in model[words_1_3]:
+        model[words_1_3][word_2] /= float(count)
+
+with open('test-A/in.tsv', encoding='utf-8') as file_in, open('test-A/out.tsv', 'w', encoding='utf-8') as file_out:
     for line_in in file_in:
         _, _, _, _, _, _, before, after = line_in.split('\t')
-        before = word_tokenize(before.replace('\\n', '\n'))
-        after = word_tokenize(after.replace('\\n', '\n'))
-        file_out.write(predict(before[-1], after[0]) + '\n')
+        word_before_in, word_after_in = preprocess(before)[-1], preprocess(after)[0]
+        file_out.write(predict(word_before_in, word_after_in) + '\n')