result.py

@@ -1,45 +0,0 @@
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LinearRegression
-from decimal import Decimal
-import numpy as np
-import lzma
-import pandas as pd
-import sys
-if sys.version_info[0] < 3: 
-    from StringIO import StringIO
-else:
-    from io import StringIO
-
-def openXZ(path):
-    with lzma.open(path, mode='rt') as f:
-      return f.readlines()
-
-def readFile(path):
-    with open(path) as source:
-          return source.readlines()
-
-def toArr(a):
-  return [x.split("\t") for x in a]
-
-def getLinearRegresion(dataPath):
-  inPath = dataPath + "/in.tsv"
-  outPath = dataPath + "/out.tsv"
-  tmpAr = toArr(readFile(inPath))
-  inDf = pd.DataFrame(data=tmpAr)
-  dataVec = vectorizer.transform(inDf[0])
-  evaluate = lg.predict(dataVec)
-  with open(outPath, 'w') as file:
-    for e in evaluate:
-      file.write("%f\n" % e)
-
-vectorizer = TfidfVectorizer()
-lg = LinearRegression()
-tmp = toArr(openXZ("./retroc2/train/train.tsv.xz"))
-train = pd.DataFrame(data=tmp)
-train = train.astype({0: np.number, 1: np.number})
-dateMean = (train[0] + train[1]) / 2
-trainVec = vectorizer.fit_transform(train[4])
-lg.fit(trainVec, dateMean)
-getLinearRegresion("./retroc2/dev-0")
-getLinearRegresion("./retroc2/dev-1")
-getLinearRegresion("./retroc2/test-A")