auta-public/Untitled.py

#!/usr/bin/env python
# coding: utf-8

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import numpy as np
import matplotlib
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error


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#with open('train/train.tsv') as file:
 #   for line in file.readlines()[:10]:
  #      print(line)


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#with open('names') as file:
 #   for line in file.readlines():
  #          header.append(line.strip())


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#train


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with open('names') as file:
    header = file.read().rstrip('\n').split('\t')

train_path='train/train.tsv'



train = pd.read_csv(train_path, sep='\t', names=header)
#removing discrete value
train.drop('brand', inplace=True, axis=1)
train.drop('engineType', inplace=True, axis=1)


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#output
y_train = pd.DataFrame(train['price'])


#removing output
train.drop('price', inplace=True, axis=1)
x_train = pd.DataFrame(train)

model = LinearRegression()
model.fit(x_train, y_train)

header=['price','year','brand','engineType','engineCapacity']


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#dev


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dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=header)


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print(dev)


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with open('dev-0/expected.tsv', 'r') as file:
    y_dev = np.array([float(x.rstrip('\n')) for x in file.readlines()])


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dev.drop('brand', inplace=True, axis=1)
dev.drop('engineType', inplace=True, axis=1)


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print(dev)


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x_dev = pd.DataFrame(dev)

predict_dev = model.predict(x_dev)
print(predict_dev)


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predict_dev.tofile('dev-0/out.tsv', sep='\n') 


# # RMSE dla dev-0

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error = np.sqrt(mean_squared_error(y_dev, predict_dev))
print(error)


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#test


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pd.DataFrame(predict).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)


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test=pd.read_csv('test-A/in.tsv', sep='\t', names=header)
print(test)


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test.drop('brand', inplace=True, axis=1)
test.drop('engineType', inplace=True, axis=1)
y_expected = pd.DataFrame(test['price'])

y_expected.to_csv('test-A/expected.tsv', sep='\t', encoding='utf-8')


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print(test)


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x_test = pd.DataFrame(test)

predict_test = model.predict(x_test)
pd.DataFrame(predict_test).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)


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predict_test.tofile('test-A/out.tsv', sep='\n') 


# # RMSE dla dev-0

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error = np.sqrt(mean_squared_error(y_dev, predict_dev))
print(error)


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