forked from kubapok/auta-public
30 lines
1.0 KiB
Python
30 lines
1.0 KiB
Python
from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from sklearn.pipeline import make_pipeline
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from sklearn import preprocessing
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from sklearn import linear_model
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import pandas as pd
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import numpy as np
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myT = pd.read_csv('train/train.tsv', sep='\t',
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names = ['price','mileage','year','brand','engineType','engineCapacity'])
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datF = pd.DataFrame(myT,
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columns = ['price','mileage','year','brand','engineType','engineCapacity'])
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y = datF[['price']]
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x = datF[['year','mileage','engineCapacity']]
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reg = linear_model.LinearRegression()
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reg.fit(x, y)
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put = pd.read_csv('test-A/in.tsv', sep = '\t',
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names = ['mileage','year','brand','engineType','engineCapacity'])
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datF = pd.DataFrame(put,
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columns = ['mileage','year','brand','engineType','engineCapacity'])
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r = datF[['year','mileage','engineCapacity']]
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y1 = reg.predict(r)
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y1 = np.concatenate(y1)
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tmp = np.array2string(y1, precision = 5,
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separator = '\n', suppress_small = True)
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tmp = tmp.lstrip('[').rstrip(']')
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f = open("test-A/out.tsv", "a")
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f.write(tmp)
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