test 7 version

model.py

@@ -1,8 +1,9 @@
-import lzma
 from nltk.tokenize import word_tokenize
 from nltk import trigrams
 import string
 from collections import defaultdict, Counter
+import pandas as pd
+import csv
 
 
 trigrams_list = []
@@ -10,7 +11,8 @@ model = defaultdict(lambda: defaultdict(lambda: 0))
 
 
 def preprocess(text):
-    _text = text.lower().replace('\\n', ' ').strip()
+    _text = str(text)
+    _text = _text.lower().replace("-\\n", "").replace('\\n', ' ').strip()
     for character in _text:
         if character not in string.ascii_lowercase + ' ':
             _text = _text.replace(character, '')
@@ -21,7 +23,6 @@ def preprocess(text):
 
 
 def predict(word_before, word_after):
-    return 'the:1.0'
     prob_list = dict(Counter(model[(word_before, word_after)]).most_common(5)).items()
     predictions = []
     prob_sum = 0.0
@@ -31,56 +32,58 @@ def predict(word_before, word_after):
     if prob_sum == 0.0:
         return 'the:0:2 be:0.2 to:0.2 of:0.15 and:0.15 :0.1'
     elif prob_sum < 1.0:
-        predictions.append(f':{1.0 - prob_sum}')
+        predictions.append(f':{max(1 - prob_sum, 0.01)}')
     return ' '.join(predictions)
 
 
-with open('train/in.tsv', 'w', encoding='utf-8') as file:
+file_in = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
-    print('dekompresja pliku')
+file_expected = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
-    text = lzma.open('train/in.tsv.xz').read().decode('utf-8')
-    file.write(text)
 
-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.iterrows(), file_expected.iterrows())):
-    for index, (line_in, expected) in enumerate(zip(file_in, file_expected)):
+    if index % 1000 == 0:
-        if index % 1000 == 0:
+        print('zbieranie trigramów', index)
-            print('zbieranie trigramów', index)
+    before = line_in[1][6]
-        _, _, _, _, _, _, before, after = line_in.split('\t')
+    after = line_in[1][7]
-        before, expected, after = preprocess(before), preprocess(expected), preprocess(after)
+    expected = expected[1][0]
-        words = before + expected + after
+    before, expected, after = preprocess(before), preprocess(expected), preprocess(after)
-        trigrams_list += trigrams(words, pad_right=True, pad_left=True)
+    words = before + expected + after
-        length = len(trigrams_list)
+    trigrams_list += trigrams(words, pad_right=True, pad_left=True)
-        print('zbieranie trigramów:', length)
+    length = len(trigrams_list)
-        if length > 1000000:
+
-            break
+trigrams_len = len(trigrams_list)
 
 for index, trigram in enumerate(trigrams_list):
     if index % 100000 == 0:
-        print('uczenie modelu', index)
+        print(f'uczenie modelu: {index / trigrams_len}')
-    if not trigram[0] or not trigram[1] or not trigram[2]:
+    if trigram[0] and trigram[1] and trigram[2]:
-        continue
+        model[(trigram[0], trigram[2])][trigram[1]] += 1
-    model[(trigram[0], trigram[2])][trigram[1]] += 1
-    if index == 999999:
-        break
 
 
+model_len = len(model)
+
 for index, words_1_3 in enumerate(model):
     if index % 100000 == 0:
-        print('normalizacja', index)
+        print(f'normalizacja: {index / model_len}')
     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:
+file_in = pd.read_csv('test-A/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+with open('test-A/out.tsv', 'w', encoding='utf-8') as file_out:
     print('zapisywanie test-A')
-    for line_in in file_in:
+    for line_in in file_in.iterrows():
-        _, _, _, _, _, _, before, after = line_in.split('\t')
+        before = line_in[1][6]
+        after = line_in[1][7]
         word_before_in, word_after_in = preprocess(before)[-1], preprocess(after)[0]
         file_out.write(predict(word_before_in, word_after_in) + '\n')
 
-with open('dev-0/in.tsv', encoding='utf-8') as file_in, open('dev-0/out.tsv', 'w', encoding='utf-8') as file_out:
+
+file_in = pd.read_csv('dev-0/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
+with open('dev-0/out.tsv', 'w', encoding='utf-8') as file_out:
     print('zapisywanie dev-0')
-    for line_in in file_in:
+    for line_in in file_in.iterrows():
-        _, _, _, _, _, _, before, after = line_in.split('\t')
+        before = line_in[1][6]
+        after = line_in[1][7]
         word_before_in, word_after_in = preprocess(before)[-1], preprocess(after)[0]
         file_out.write(predict(word_before_in, word_after_in) + '\n')