result final

result.py

@@ -0,0 +1,45 @@
+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")