forked from kubapok/auta-public
62 lines
2.6 KiB
Python
62 lines
2.6 KiB
Python
import pandas as pd
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from pandas import DataFrame
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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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import numpy as np
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df = pd.read_csv("train/train.tsv", header=None, sep="\t", error_bad_lines=False, names=['price', 'mileage', 'year',
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'brand', 'engineType',
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'engineCapacity'])
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dev0 = pd.read_csv("dev-0/in.tsv", header=None, sep="\t", error_bad_lines=False, names=['mileage', 'year',
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'brand', 'engineType',
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'engineCapacity'])
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testA = pd.read_csv("test-A/in.tsv", header=None, sep="\t", error_bad_lines=False, names=['mileage', 'year',
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'brand', 'engineType',
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'engineCapacity'])
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expected = pd.read_csv("dev-0/expected.tsv", header=None, sep="\t", error_bad_lines=False, names=['price'])
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df = df[['price', 'year', 'mileage', 'engineCapacity']]
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min_val = np.min(df)
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max_val = np.max(df)
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df = (df - min_val) / (max_val - min_val)
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Y = df[['price']]
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X = df[['year', 'mileage', 'engineCapacity']]
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model = LinearRegression().fit(X, Y)
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dev0 = (dev0[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
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testA = (testA[['year', 'mileage', 'engineCapacity']] - min_val) / (max_val - min_val)
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predicted_dev0 = model.predict(dev0[['year', 'mileage', 'engineCapacity']])
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predicted_testA = model.predict(testA[['year', 'mileage', 'engineCapacity']])
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predicted_denormalized = []
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for pred in predicted_dev0:
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denorm = pred[0] * (max_val[0] - min_val[0]) + min_val[0]
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predicted_denormalized.append(denorm)
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with open("dev-0/out.tsv", "w") as file:
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for pred in predicted_denormalized:
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file.write(str(pred) + "\n")
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predicted_denormalizedA = []
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for pred in predicted_testA:
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denorm = pred[0] * (max_val[0] - min_val[0]) + min_val[0]
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predicted_denormalizedA.append(denorm)
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with open("test-A/out.tsv", "w") as file:
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for pred in predicted_denormalizedA:
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file.write(str(pred) + "\n")
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predicted_denormalized = DataFrame(predicted_denormalized, columns=['pred'])
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error = mean_squared_error(expected, predicted_denormalized)
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for exp, pred in zip(expected.values, predicted_denormalized.values):
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print(exp, pred)
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f = open("dev0_rmse.txt", "w")
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f.write(str(error))
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f.close()
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print(error) |