Zaktualizuj 'Main.py'

Main.py

@@ -0,0 +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')
+
+#Outcome: 33956 for prices >10000