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Zaktualizuj 'Main.py'

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Damian Bregier 2021-05-26 12:24:19 +02:00
parent 69b5c6854c
commit 2dfefc4943
1 changed files with 44 additions and 44 deletions
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Main → Main.py
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#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