forked from kubapok/auta-public
45 lines
1.6 KiB
Plaintext
45 lines
1.6 KiB
Plaintext
#basic imports
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import pandas
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from sklearn.linear_model import LinearRegression
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#basic paths
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openTrain = './train/train.tsv'
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openDev = './dev-0/in.tsv'
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openTest = './test-A/in.tsv'
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#read from files
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with open('./names') as f_names:
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names = f_names.read().rstrip('\n').split('\t')
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read0 = pandas.read_table(openTrain, sep='\t', names=names)
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read1 = pandas.read_table(openDev, sep='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
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#basic normalization & filtering
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trainSet = pandas.get_dummies(read0, columns=['engineType'])
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categories1 = trainSet.select_dtypes(include=object).columns.values
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for c in categories1:
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trainSet[c] = trainSet[c].astype('category').cat.codes
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trainSet = trainSet.loc[(trainSet['price'] > 10000)] #to avoid suspicious observations
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#for some reason this value gives the smallest RMSE according to geval, while smaller or bigger
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#price gives RMSE >34k
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#Model training
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X = trainSet.loc[:, trainSet.columns != 'price']
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solution = LinearRegression().fit(X, trainSet['price'])
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devSet = pandas.get_dummies(read1, columns=['engineType'])
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categories2 = devSet.select_dtypes(include=object).columns.values
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for c in categories2:
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devSet[c] = devSet[c].astype('category').cat.codes
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predict = solution.predict(devSet)
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predict.tofile("./dev-0/out.tsv", sep='\n')
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testSet = pandas.get_dummies(read1, columns=['engineType'])
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categories3 = testSet.select_dtypes(include=object).columns.values
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for c in categories3:
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testSet[c] = testSet[c].astype('category').cat.codes
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predict = solution.predict(devSet)
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predict.tofile("./test-A/out.tsv", sep='\n')
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#Outcome: 33956 for prices >10000 |