run.py

@@ -1,39 +0,0 @@
-import string
-import os
-
-import pandas as pd
-import numpy as np
-from sklearn.feature_extraction.text import TfidfVectorizer
-from stop_words import get_stop_words
-from sklearn.linear_model import LinearRegression
-from scipy.sparse import vstack
-from sklearn.utils import shuffle
-
-data_raw = pd.read_csv('retroc2/train/train.tsv', delimiter = '\t', header = None, names = ['date_from', 'date_to', 'title', 'source', 'text'])
-
-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, max_features=30000, max_df=0.35)
-tfs = vectorizer.fit_transform(data_raw.text)
-data_X = vstack([tfs,tfs])
-
-data_y = np.concatenate((data_raw.date_from, data_raw.date_to), axis = 0)
-data_X, data_y = shuffle(data_X, data_y, random_state=42)
-
-clf = LinearRegression()
-
-clf.fit(data_X,data_y)
-
-import csv
-for dir in ['retroc2/dev-0/', 'retroc2/dev-1/', 'retroc2/test-A/']:
-  test_raw = pd.read_csv(dir+'in.tsv', delimiter = '\t', header = None, names = ['text'],quoting=csv.QUOTE_NONE)
-  vectorized = vectorizer.transform(test_raw.text)
-  X_test = vectorized.toarray()
-  y_predicted = clf.predict(X_test)
-  np.savetxt(dir+"out.tsv", y_predicted, delimiter="\t")