#!/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)


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len(dev_y0[0])


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len(dev_x0[0])


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len(dev_y1[0])


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len(dev_x1[0])


# In[43]:


train_x = train[4]
train_y_mean = (train.iloc[:, 0] + train.iloc[:, 1])/2


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train_y_mean = train_y_mean[:30000]
train_x = train_x[:30000]


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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])


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dev0_y_pred = lm.predict(X_dev0_)
dev1_y_pred = lm.predict(X_dev1_)
test_y_pred = lm.predict(X_test_)


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print(dev_y0[:19998])


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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)


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print(dev_y0[:10])


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type(dev0_y_pred)


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np.savetxt("out.tsv",dev0_y_pred, delimiter="\t", fmt='%1.8f')


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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')


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