61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
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import subprocess
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import sys
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import pandas as pd
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import os
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import numpy as np
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try:
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dataset_path = sys.argv[1]
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except Exception as e:
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print("Exception while retrieving dataset path")
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print(e)
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def divide_dataset(dataset, path):
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"""Split dataset to dev, train, test datasets. """
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print('Shuffle dataset...')
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shuf_path = 'data/Car_Prices_Poland_Kaggle_shuf.csv'
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os.system(f'tail -n +2 {path} | shuf > {shuf_path}')
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len1 = len(dataset) // 6
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len2 = (len1 * 2) + 1
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print('Dividing dataset...')
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os.system(f'head -n {len1} {shuf_path} > data/Car_Prices_Poland_Kaggle_dev.csv')
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os.system(f'head -n {len1} {shuf_path} | tail -n {len1} > data/Car_Prices_Poland_Kaggle_test.csv')
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os.system(f'tail -n +{len2} {shuf_path} > data/Car_Prices_Poland_Kaggle_train.csv')
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os.system(f'rm {shuf_path}')
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print("Len match: " + str(sum([len1 * 2, len2]) == len(dataset)))
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os.system('cat Car_Prices_Poland_Kaggle_train.csv | wc -l')
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os.system('cat Car_Prices_Poland_Kaggle_dev.csv | wc -l')
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os.system('cat Car_Prices_Poland_Kaggle_test.csv | wc -l')
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print('Dataset devided')
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def normalize_dataset(dataset):
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"""Drop unnecessary columns and set numeric values to [0,1] range"""
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print(f'--------------- Initial dataset length ---------------')
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print(len(dataset))
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# drop columns
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dataset.drop(columns=["Unnamed: 0", "generation_name"], inplace=True)
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dataset = dataset.dropna()
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# normalize numbers to [0, 1]
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for column in dataset.columns:
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if isinstance(dataset.iloc[1][column], np.int64) or isinstance(dataset.iloc[1][column], np.float64):
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dataset[column] = (dataset[column] - dataset[column].min()) / (dataset[column].max() - dataset[column].min())
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return dataset
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cars = pd.read_csv(dataset_path)
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df = pd.DataFrame(cars)
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df = normalize_dataset(df)
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divide_dataset(df, dataset_path)
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