linear reg

linearRegression.py

@@ -0,0 +1,39 @@
+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')
+# --------------