run.py

@@ -1,86 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# In[171]:
-
-
-import pandas as pd
-from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
-from sklearn.naive_bayes import MultinomialNB
-
-
-# In[172]:
-
-
-f = open("train\in.tsv", "r", encoding='utf-8')
-lines = f.readlines()
-x_train = pd.DataFrame(lines)
-x_train.rename(columns = {0 : 'text'}, inplace = True)
-
-
-# In[173]:
-
-
-y_train = pd.read_csv('train\expected.tsv', sep='\t', names=['paranormal'], encoding='utf-8')
-
-
-# In[174]:
-
-
-tfidf_vectorizer=TfidfVectorizer(max_df=0.95, max_features=500)
-tfidf_vectorizer.fit_transform(x_train['text'].values)
-x_train_prepared = tfidf_vectorizer.transform(x_train['text'].values).toarray()
-
-
-# In[175]:
-
-
-mnb = MultinomialNB()
-model_mnb = mnb.fit(x_train_prepared, y_train.values.ravel())
-
-
-# In[176]:
-
-
-f = open("dev-0\in.tsv", "r", encoding='utf-8')
-lines = f.readlines()
-x_dev = pd.DataFrame(lines)
-x_dev.rename(columns = {0 : 'text'}, inplace = True)
-
-x_dev_prepared = tfidf_vectorizer.transform(x_dev['text'].values).toarray()
-
-y_dev = pd.read_csv('dev-0\expected.tsv', sep='\t', names=['paranormal'], encoding='utf-8')
-
-y_dev_pred = model_mnb.predict(x_dev_prepared)
-
-
-# In[177]:
-
-
-file = open('dev-0\out.tsv', 'w')
-for y in y_dev_pred:
-    file.write(f'{y}\n')
-file.close()
-
-
-# In[179]:
-
-
-f = open("test-A\in.tsv", "r", encoding='utf-8')
-lines = f.readlines()
-x_test = pd.DataFrame(lines)
-x_test.rename(columns = {0 : 'text'}, inplace = True)
-
-x_test_prepared = tfidf_vectorizer.transform(x_test['text'].values).toarray()
-
-y_test_pred = model_mnb.predict(x_test_prepared)
-
-
-# In[180]:
-
-
-file = open('test-A\out.tsv', 'w')
-for y in y_test_pred:
-    file.write(f'{y}\n')
-file.close()
-