result.py

@@ -1,30 +0,0 @@
-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")