71 lines
3.0 KiB
Plaintext
71 lines
3.0 KiB
Plaintext
import subprocess
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import zipfile
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import os
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import pandas as pd
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import re
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from sklearn.model_selection import train_test_split
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# Skrypt 1 funkcje
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def download_kaggle_dataset(dataset_id, destination_folder):
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try:
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result = subprocess.run(["kaggle", "datasets", "download", "-d", dataset_id, "-p", destination_folder], check=True, capture_output=True, text=True)
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zip_filename = re.search(r"(\S+\.zip)", result.stdout).group(1)
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print(f"Dataset {dataset_id} successfully downloaded.")
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return os.path.join(destination_folder, zip_filename)
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except subprocess.CalledProcessError as e:
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print(f"Error downloading dataset {dataset_id}: {e}")
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return None
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def unzip_file(zip_filepath, destination_folder):
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try:
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with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:
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zip_ref.extractall(destination_folder)
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print(f"Files extracted to {destination_folder}.")
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except Exception as e:
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print(f"Error unzipping file {zip_filepath}: {e}")
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def combine_csv_files(train_file, test_file, output_file):
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try:
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train_df = pd.read_csv(train_file)
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test_df = pd.read_csv(test_file)
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combined_df = pd.concat([train_df, test_df], ignore_index=True)
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combined_df.to_csv(output_file, index=False)
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print(f"Combined CSV files saved to {output_file}.")
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except Exception as e:
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print(f"Error combining CSV files: {e}")
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# Skrypt 2 funkcje
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def split_data(data, train_ratio, dev_ratio, random_seed=42):
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train_data, temp_data = train_test_split(data, train_size=train_ratio, random_state=random_seed)
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dev_data, test_data = train_test_split(temp_data, train_size=dev_ratio / (1 - train_ratio), random_state=random_seed)
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return train_data, dev_data, test_data
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def main():
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# Skrypt 1
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dataset_id = "iabhishekofficial/mobile-price-classification"
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destination_folder = r"C:\studia\InżynieriaUczeniaMaszynowego"
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zip_filepath = download_kaggle_dataset(dataset_id, destination_folder)
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if zip_filepath is not None:
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unzip_file(zip_filepath, destination_folder)
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train_file = os.path.join(destination_folder, "train.csv")
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test_file = os.path.join(destination_folder, "test.csv")
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output_file = os.path.join(destination_folder, "combined.csv")
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combine_csv_files(train_file, test_file, output_file)
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# Skrypt 2
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data = pd.read_csv(output_file)
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train_data, dev_data, test_data = split_data(data, train_ratio=0.6, dev_ratio=0.2)
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output_dir = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InżynieriaUczeniaMaszynowego")
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train_data.to_csv(os.path.join(output_dir, 'Train1.csv'), index=False)
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dev_data.to_csv(os.path.join(output_dir, 'Dev1.csv'), index=False)
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test_data.to_csv(os.path.join(output_dir, 'Test1.csv'), index=False)
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print(f"Liczba wierszy w pliku Train1.csv: {len(train_data)}")
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print(f"Liczba wierszy w pliku Dev1.csv: {len(dev_data)}")
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print(f"Liczba wierszy w pliku Test1.csv: {len(test_data)}")
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if __name__ == "__main__":
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main() |