21 geval RMSE score

result.py

@@ -0,0 +1,30 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+import numpy as np
+import pandas as pd
+import csv
+
+def getData(path):
+    with open(path, encoding="utf-8") as source:
+          return source.readlines()
+
+def changeToDf(input):
+  return pd.read_csv(input, sep="\t")
+
+vectorizer = TfidfVectorizer()
+linear = LinearRegression()
+train = pd.read_csv("./train/train.tsv", sep="\t", names=['start_date', 'end_date', 'title', 'sort_title', 'data'])
+mean = (train['start_date'] + train['end_date']) / 2
+tv = vectorizer.fit_transform(train['data'])
+linear.fit(tv, mean)
+
+def calculateResult(in_, out):
+  tmp = getData(in_)
+  df = pd.DataFrame(data = tmp)
+  data = vectorizer.transform(df[0])
+  evaluate = linear.predict(data)
+  np.savetxt(out, evaluate, fmt='%f', delimiter='\n')
+
+calculateResult("./dev-0/in.tsv", "./dev-0/out.tsv")
+calculateResult("./dev-1/in.tsv", "./dev-1/out.tsv")
+calculateResult("./test-A/in.tsv", "./test-A/out.tsv")