forked from kubapok/auta-public
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
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import numpy as np
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import pandas as pd
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from scipy.sparse import data
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from sklearn import linear_model
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from sklearn import preprocessing
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn import linear_model
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import csv
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import pandas as pd
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regression = linear_model.LinearRegression()
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train_file = pd.read_csv('train/train.tsv', delimiter='\t', names=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
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train_data_frame = pd.DataFrame(train_file, columns=['price', 'mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
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Y = train_data_frame[['price']]
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X = train_data_frame[['year', 'mileage', 'engineCapacity']]
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regression.fit(X, Y)
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in_file = pd.read_csv('test-A/in.tsv', delimiter='\t', names=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
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in_data_frame = pd.DataFrame(in_file, columns=['mileage', 'year', 'brand', 'engineType', 'engineCapacity'])
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reshape = in_data_frame[['year', 'mileage', 'engineCapacity']]
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y_predict = regression.predict(reshape)
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y_predict = np.concatenate(y_predict)
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labels = np.array2string(y_predict, separator='\n', suppress_small=True)
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file_out = open("test-A/out.tsv", 'w')
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file_out.write(labels[1:-1])
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with open("test-A/out.tsv", 'r') as fix_space:
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lines = fix_space.readlines()
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lines = [line.replace(' ', '') for line in lines]
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with open("test-A/out.tsv", 'w') as fix_space:
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fix_space.writelines(lines)
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