forked from kubapok/auta-public
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import pandas as pd
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import numpy as np
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from pathlib import Path
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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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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cars_train = pd.read_csv('train/train.tsv', sep='\t', names=names)
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cars_train = pd.get_dummies(cars_train, columns=['engineType'])
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y_train = pd.DataFrame(cars_train['price'])
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cars_train.drop('price', inplace=True, axis=1)
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cars_train.drop('brand', inplace=True, axis=1)
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x_train = pd.DataFrame(cars_train)
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model = LinearRegression()
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model.fit(x_train, y_train)
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names.remove('price')
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cars_dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=names)
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with open('dev-0/expected.tsv', 'r') as dev_exp_f:
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Y_dev = np.array([float(x.rstrip('\n')) for x in dev_exp_f.readlines()])
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cars_dev = pd.get_dummies(cars_dev, columns=['engineType'])
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cars_dev.drop('brand', inplace=True, axis=1)
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X_dev = pd.DataFrame(cars_dev)
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Y_dev_predicted = model.predict(X_dev)
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print(Y_dev_predicted)
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pd.DataFrame(Y_dev_predicted).to_csv('dev-0/out.tsv', sep='\t', index=False, header=False)
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cars_test=pd.read_csv('test-A/in.tsv', sep='\t', names=names)
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cars_test = pd.get_dummies(cars_test, columns=['engineType'])
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cars_test.drop('brand', inplace=True, axis=1)
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X_test = pd.DataFrame(cars_test)
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Y_test_predicted = model.predict(X_test)
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pd.DataFrame(Y_test_predicted).to_csv('test-A/out.tsv', sep='\t', index=False, header=False)
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error = np.sqrt(mean_squared_error(Y_dev, Y_dev_predicted))
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print(error)
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