linear regression

linear-regression.py

@@ -0,0 +1,48 @@
+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")
+    dev_1 = pd.read_csv("dev-1/in.tsv", error_bad_lines = False, header = None, sep = "\t")
+    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(dev_0['data'])
+    prediction_dev = lin_reg.predict(evaluate_dev)
+    pd.DataFrame(prediction_dev).to_csv('dev-0/out.tsv', sep = "\t", index = False, header = False)
+
+    # predykcja dla test-A
+    evaluate_test = tf(test['data'])
+    prediction_test = lin_reg.predict(evaluate_test)
+    pd.DataFrame(prediction_test).to_csv('test-A/out.tsv', sep = "\t", index = False, header = False)
+
+
+    return  None
+
+ 
+if __name__ == "__main__":
+    linear_regression()
+
+# geval: 21.80