linear-regression.py

@@ -1,48 +0,0 @@
-from re import L
-import pandas as pd
-import numpy as np
-import csv
-from sklearn.linear_model import LinearRegression
-from stop_words import get_stop_words
-from sklearn.feature_extraction.text import TfidfVectorizer
- 
-def linear_regression():
-
-    # odczyt z plików
-    colnames_train = ['start_date', 'end_date', 'title', 'sort_title', 'data']
-    colnames_test = ['data']
-
-    train = pd.read_csv("train/train.tsv", names = colnames_train, sep = "\t")
-    dev_0 = pd.read_csv("dev-0/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE)
-    dev_1 = pd.read_csv("dev-1/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE,)
-    test = pd.read_csv("test-A/in.tsv", names = colnames_test, sep = "\t")
-
-    # stworzenie instancji TFIDF i regresji liniowej
-    tf = TfidfVectorizer(stop_words=get_stop_words('polish'))
-    lin_reg = LinearRegression()
-
-    # wydobycie daty
-    date = (train['start_date'] + train['end_date']) / 2
-
-    # regresja liniowa
-    train_vec = tf.fit_transform(train['data'])
-    lin_reg.fit(train_vec, date)
-
-    # predykcja dla dev-0
-    evaluate_dev = tf.transform(dev_0['data'])
-    prediction_dev = lin_reg.predict(evaluate_dev)
-    pd.DataFrame(prediction_dev).to_csv('dev-0/out2.tsv', sep = "\t", index = False, header = False)
-
-    # predykcja dla test-A
-    evaluate_test = tf.transform(test['data'])
-    prediction_test = lin_reg.predict(evaluate_test)
-    pd.DataFrame(prediction_test).to_csv('test-A/out2.tsv', sep = "\t", index = False, header = False)
-
-
-    return  None
-
- 
-if __name__ == "__main__":
-    linear_regression()
-
-# geval: 21.80