my brilliant solution

foo.py

@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[24]:
+
+
+import pandas as pd 
+import numpy as np
+import math
+from sklearn.pipeline import make_pipeline
+import os
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+import csv
+
+
+# In[39]:
+
+
+train = pd.read_csv('train/train.tsv', header=None, sep='\t')
+dev_x0 = pd.read_csv('dev-0/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+dev_y0 = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t',quoting=csv.QUOTE_NONE, error_bad_lines=False)
+dev_x1 = pd.read_csv('dev-1/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+dev_y1 = pd.read_csv('dev-1/expected.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t', quoting=csv.QUOTE_NONE, error_bad_lines=False)
+
+
+# In[26]:
+
+
+len(dev_y0[0])
+
+
+# In[27]:
+
+
+len(dev_x0[0])
+
+
+# In[40]:
+
+
+len(dev_y1[0])
+
+
+# In[41]:
+
+
+len(dev_x1[0])
+
+
+# In[43]:
+
+
+train_x = train[4]
+train_y_mean = (train.iloc[:, 0] + train.iloc[:, 1])/2
+
+
+# In[49]:
+
+
+train_y_mean = train_y_mean[:30000]
+train_x = train_x[:30000]
+
+
+# In[51]:
+
+
+vectorizer = TfidfVectorizer()
+X_train_tfidf = vectorizer.fit_transform(train_x)
+
+
+# In[52]:
+
+
+lm = LinearRegression()
+lm.fit(X_train_tfidf,train_y_mean)
+
+X_dev0_= vectorizer.transform(dev_x0[0])
+X_dev1_ = vectorizer.transform(dev_x1[0])
+X_test_ = vectorizer.transform(test_x[0])
+
+
+# In[54]:
+
+
+dev0_y_pred = lm.predict(X_dev0_)
+dev1_y_pred = lm.predict(X_dev1_)
+test_y_pred = lm.predict(X_test_)
+
+
+# In[55]:
+
+
+print(dev_y0[:19998])
+
+
+# In[58]:
+
+
+rmse_dev0 = mean_squared_error(dev_y0, dev0_y_pred, squared=False)
+rmse_dev1 = mean_squared_error(dev_y1,dev1_y_pred, squared = False)
+
+print(rmse_dev0, rmse_dev1)
+
+
+# In[18]:
+
+
+print(dev_y0[:10])
+
+
+# In[64]:
+
+
+type(dev0_y_pred)
+
+
+# In[65]:
+
+
+np.savetxt("out.tsv",dev0_y_pred, delimiter="\t", fmt='%1.8f')
+
+
+# In[66]:
+
+
+np.savetxt("out.tsv",dev1_y_pred, delimiter="\t", fmt='%1.8f')
+
+
+# In[67]:
+
+
+np.savetxt("out.tsv",test_y_pred, delimiter="\t", fmt='%1.8f')
+
+
+# In[ ]:
+
+
+
+