From 2dfefc4943fd067d48d6c4e36bae14df75128a4f Mon Sep 17 00:00:00 2001
From: Damian Bregier <dambre@st.amu.edu.pl>
Date: Wed, 26 May 2021 12:24:19 +0200
Subject: [PATCH] Zaktualizuj 'Main.py'

---
 Main => Main.py | 88 ++++++++++++++++++++++++-------------------------
 1 file changed, 44 insertions(+), 44 deletions(-)
 rename Main => Main.py (95%)

diff --git a/Main b/Main.py
similarity index 95%
rename from Main
rename to Main.py
index 68b5fdd..6e671db 100644
--- a/Main
+++ b/Main.py
@@ -1,45 +1,45 @@
-#basic imports
-import pandas
-from sklearn.linear_model import LinearRegression
-
-#basic paths
-openTrain = './train/train.tsv'
-openDev = './dev-0/in.tsv'
-openTest = './test-A/in.tsv'
-
-#read from files
-with open('./names') as f_names:
-    names = f_names.read().rstrip('\n').split('\t')
-read0 = pandas.read_table(openTrain, sep='\t', names=names)
-read1 = pandas.read_table(openDev, sep='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
-
-#basic normalization & filtering
-trainSet = pandas.get_dummies(read0, columns=['engineType'])
-categories1 = trainSet.select_dtypes(include=object).columns.values
-for c in categories1:
-    trainSet[c] = trainSet[c].astype('category').cat.codes
-trainSet = trainSet.loc[(trainSet['price'] > 10000)] #to avoid suspicious observations
-#for some reason this value gives the smallest RMSE according to geval, while smaller or bigger
-#price gives RMSE >34k
-
-#Model training
-X = trainSet.loc[:, trainSet.columns != 'price']
-solution = LinearRegression().fit(X, trainSet['price'])
-
-devSet = pandas.get_dummies(read1, columns=['engineType'])
-categories2 = devSet.select_dtypes(include=object).columns.values
-for c in categories2:
-    devSet[c] = devSet[c].astype('category').cat.codes
-
-predict = solution.predict(devSet)
-predict.tofile("./dev-0/out.tsv", sep='\n')
-testSet = pandas.get_dummies(read1, columns=['engineType'])
-
-categories3 = testSet.select_dtypes(include=object).columns.values
-for c in categories3:
-    testSet[c] = testSet[c].astype('category').cat.codes
-
-predict = solution.predict(devSet)
-predict.tofile("./test-A/out.tsv", sep='\n')
-
+#basic imports
+import pandas
+from sklearn.linear_model import LinearRegression
+
+#basic paths
+openTrain = './train/train.tsv'
+openDev = './dev-0/in.tsv'
+openTest = './test-A/in.tsv'
+
+#read from files
+with open('./names') as f_names:
+    names = f_names.read().rstrip('\n').split('\t')
+read0 = pandas.read_table(openTrain, sep='\t', names=names)
+read1 = pandas.read_table(openDev, sep='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
+
+#basic normalization & filtering
+trainSet = pandas.get_dummies(read0, columns=['engineType'])
+categories1 = trainSet.select_dtypes(include=object).columns.values
+for c in categories1:
+    trainSet[c] = trainSet[c].astype('category').cat.codes
+trainSet = trainSet.loc[(trainSet['price'] > 10000)] #to avoid suspicious observations
+#for some reason this value gives the smallest RMSE according to geval, while smaller or bigger
+#price gives RMSE >34k
+
+#Model training
+X = trainSet.loc[:, trainSet.columns != 'price']
+solution = LinearRegression().fit(X, trainSet['price'])
+
+devSet = pandas.get_dummies(read1, columns=['engineType'])
+categories2 = devSet.select_dtypes(include=object).columns.values
+for c in categories2:
+    devSet[c] = devSet[c].astype('category').cat.codes
+
+predict = solution.predict(devSet)
+predict.tofile("./dev-0/out.tsv", sep='\n')
+testSet = pandas.get_dummies(read1, columns=['engineType'])
+
+categories3 = testSet.select_dtypes(include=object).columns.values
+for c in categories3:
+    testSet[c] = testSet[c].astype('category').cat.codes
+
+predict = solution.predict(devSet)
+predict.tofile("./test-A/out.tsv", sep='\n')
+
 #Outcome: 33956 for prices >10000
\ No newline at end of file