Download split_csv.py from deepsynthbody/deepfake_ecg_full_train_validation_test: direct link, hf CLI and curl.
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https://huggingface.co/datasets/deepsynthbody/deepfake_ecg_full_train_validation_test/resolve/main/split_csv.py
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hf download hf://datasets/deepsynthbody/deepfake_ecg_full_train_validation_test/split_csv.py
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curl -L -o split_csv.py https://huggingface.co/datasets/deepsynthbody/deepfake_ecg_full_train_validation_test/resolve/main/split_csv.py
2.57 kB
| import pandas as pd | |
| import os | |
| import shutil | |
| def split_csv(input_file, train_file, validate_file, test_file, train_size, validate_size, test_size): | |
| # Read the input CSV file | |
| data = pd.read_csv(input_file) | |
| # Split the data into train, validate, and test subsets | |
| train_data = data.iloc[:train_size] | |
| validate_data = data.iloc[train_size:train_size + validate_size] | |
| test_data = data.iloc[train_size + validate_size:train_size + validate_size + test_size] | |
| # Save the subsets to separate CSV files | |
| train_data.to_csv(train_file, index=False) | |
| validate_data.to_csv(validate_file, index=False) | |
| test_data.to_csv(test_file, index=False) | |
| def copy_ecg_files(csv_file, destination_folder, src_folder): | |
| # Read the CSV file | |
| data = pd.read_csv(csv_file) | |
| # Create the destination folder if it doesn't exist | |
| if not os.path.exists(destination_folder): | |
| os.makedirs(destination_folder) | |
| # Copy the corresponding ECG files to the destination folder | |
| for index, row in data.iterrows(): | |
| ecg_file = str(row['patid']) + ".asc" # Replace 'ecg_filename' with the appropriate column name containing the ECG file names | |
| src_path = os.path.join(src_folder, ecg_file) | |
| dst_path = os.path.join(destination_folder, ecg_file) | |
| shutil.copy(src_path, dst_path) | |
| if __name__ == '__main__': | |
| # Set the input CSV file and the source folder | |
| dst_dir = '/work/vajira/data/deepfake_ecg_full_train_validation_test' | |
| input_file = '/work/vajira/data/Deepfake-ecg/filtered_all_normals_121977_ground_truth.csv' # Change this to the name of your CSV file | |
| train_file = f'{dst_dir}/train.csv' | |
| validate_file = f'{dst_dir}/validate.csv' | |
| test_file = f'{dst_dir}/test.csv' | |
| src_folder = '/work/vajira/data/Deepfake-ecg/filtered_all_normals_121977/from_006_chck_2500_150k_filtered_all_normals_121977' # Change this to the name of the folder containing the ECG files | |
| # Set the sizes of the train, validate, and test subsets from full size of the dataset 121977 | |
| train_size = 97581 # 80% of 121977=97581 | |
| validate_size = 12198 # 10% of 121977=12198 | |
| test_size = 12198 # 10% of 121977=12198 | |
| split_csv(input_file, train_file, validate_file, test_file, train_size, validate_size, test_size) | |
| # Call the copy_ecg_files function to copy the ECG files into the corresponding folders | |
| copy_ecg_files(train_file, f'{dst_dir}/train', src_folder) | |
| copy_ecg_files(validate_file, f'{dst_dir}/validation', src_folder) | |
| copy_ecg_files(test_file, f'{dst_dir}/test', src_folder) | |