From b20466a23f74872299a0d82e9411918c67b2d06a Mon Sep 17 00:00:00 2001
From: "Maciej(Linux)" <masobkowiak@gmail.com>
Date: Mon, 11 Apr 2022 00:56:48 +0200
Subject: [PATCH] s434784

---
 run.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/run.py b/run.py
index 97f2d0c..f6e67d8 100644
--- a/run.py
+++ b/run.py
@@ -1,11 +1,11 @@
-from nltk import trigrams, word_tokenize
+from nltk import tris, word_tokenize
 import pandas as pd
 import csv
 import regex as re
 from collections import Counter, defaultdict
 
 
-train_set = pd.read_csv(
+train = pd.read_csv(
     'train/in.tsv.xz',
     sep='\t',
     on_bad_lines='skip',
@@ -14,7 +14,7 @@ train_set = pd.read_csv(
     nrows=50000)
 
 
-train_labels = pd.read_csv(
+labels = pd.read_csv(
     'train/expected.tsv',
     sep='\t',
     on_bad_lines='skip',
@@ -28,7 +28,7 @@ def data_preprocessing(text):
 
 
 def predict(before, after):
-    prediction = dict(Counter(dict(trigram[before, after])).most_common(5))
+    prediction = dict(Counter(dict(tri[before, after])).most_common(5))
     result = ''
     prob = 0.0
     for key, value in prediction.items():
@@ -52,28 +52,28 @@ def make_prediction(file):
             file_out.write(prediction + '\n')
 
 
-train_set = train_set[[6, 7]]
-train_set = pd.concat([train_set, train_labels], axis=1)
-train_set['line'] = train_set[6] + train_set[0] + train_set[7]
+train = train[[6, 7]]
+train = pd.concat([train, labels], axis=1)
+train['line'] = train[6] + train[0] + train[7]
 
 
-trigram = defaultdict(lambda: defaultdict(lambda: 0))
+tri = defaultdict(lambda: defaultdict(lambda: 0))
 
-rows = train_set.iterrows()
-rows_len = len(train_set)
+rows = train.iterrows()
+rows_len = len(train)
 for index, (_, row) in enumerate(rows):
     text = data_preprocessing(str(row['line']))
     words = word_tokenize(text)
-    for word_1, word_2, word_3 in trigrams(words, pad_right=True, pad_left=True):
+    for word_1, word_2, word_3 in tris(words, pad_right=True, pad_left=True):
         if word_1 and word_2 and word_3:
-            trigram[(word_1, word_3)][word_2] += 1
+            tri[(word_1, word_3)][word_2] += 1
 
-model_len = len(trigram)
-for index, words_1_3 in enumerate(trigram):
-    count = sum(trigram[words_1_3].values())
-    for word_2 in trigram[words_1_3]:
-        trigram[words_1_3][word_2] += 0.25
-        trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
+model_len = len(tri)
+for index, words_1_3 in enumerate(tri):
+    count = sum(tri[words_1_3].values())
+    for word_2 in tri[words_1_3]:
+        tri[words_1_3][word_2] += 0.25
+        tri[words_1_3][word_2] /= float(count + 0.25 + len(word_2))
 
 
 make_prediction('test-A')