linearRegression.py

@@ -1,39 +0,0 @@
-import gzip
-import pandas as pd 
-import numpy as np 
-from sklearn.linear_model import LinearRegression
-from sklearn.utils import shuffle
-from sklearn.metrics import accuracy_score
-
-
-def preprocess(x):
-    x = pd.concat([x, x['engineType'].str.get_dummies().astype(bool)], axis = 1 )
-    x = x.drop(['engineType','brand'], axis = 1)
-    return x
-
-baseUrl = '/home/przemek/ekstrakcja/auta-public/'
-
-data = pd.read_table(baseUrl + 'train/train.tsv', error_bad_lines=False, header= None, names=['price', 'mileage', 'year','brand','engineType', 'engineCap'])
-
-y_train = data['price']
-x_train = data.iloc[:,1:]
-x_train = preprocess(x_train)
-
-model = LinearRegression()
-model.fit(x_train, y_train)
-
-# dev-0
-x_dev = pd.read_table(baseUrl + 'dev-0/in.tsv', error_bad_lines=False, header= None, names=['mileage', 'year','brand','engineType', 'engineCap'])
-x_dev = preprocess(x_dev)
-
-y_pred = model.predict(x_dev)
-y_pred.tofile(baseUrl + 'dev-0/out.tsv', sep='\n')
-# --------------
-
-# test-A
-x_testA =  pd.read_table(baseUrl + '/test-A/in.tsv', error_bad_lines=False, header= None, names=['mileage', 'year','brand','engineType', 'engineCap'])
-x_testA = preprocess(x_testA)
-
-y_predA = model.predict(x_testA)
-y_predA.tofile(baseUrl + 'test-A/out.tsv', sep='\n')
-# --------------