Main.py

@@ -1,45 +0,0 @@
-#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