run.py

@@ -1,33 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.feature_extraction.text import TfidfVectorizer
-data_raw = pd.read_csv('train/train.tsv', delimiter = '\t', header = None,usecols=[0, 1], names = ['y', 'X'])
-
-import string
-import os
-
-from sklearn.feature_extraction.text import TfidfVectorizer
-from stop_words import get_stop_words
-
-
-def preprocess(item):
-    to_replace = '''~!@#$%^&*()_+-=[]{};\'":?/.>,<1234567890–”’'''
-    for r in to_replace:
-      item = item.replace(r, '')
-    return item.lower()
-
-
-stop_words = get_stop_words('polish') + ['aby', 'tych', 'tym', 'tyle', 'tymi', 'też']
-vectorizer = TfidfVectorizer(stop_words=stop_words, preprocessor=preprocess)
-tfs = vectorizer.fit_transform(data_raw.X)
-
-from sklearn.naive_bayes import MultinomialNB
-clf = MultinomialNB()
-
-clf.fit(tfs,data_raw.y)
-
-for dir in ['dev-0/', 'test-A/']:
-  test_raw = pd.read_csv(dir+'in.tsv', delimiter = '\t', header = None,usecols=[0], names = ['X'])
-  X_test = vectorizer.transform(test_raw.X)
-  y_predicted = clf.predict(X_test)
-  np.savetxt(dir+"out.tsv", y_predicted, delimiter="\t")