75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
import json
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import os
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import numpy as np
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# Hyperparameters
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dataset_name = 'Pulse'
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data_file_path = f'datasets/raw_data/{dataset_name}/{dataset_name}.npy'
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graph_file_path = None
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output_dir = f'datasets/{dataset_name}'
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target_channel = [0] # Target traffic flow channel
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frequency = None
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domain = 'simulated pulse data'
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feature_description = [domain]
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regular_settings = {
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'INPUT_LEN': 336,
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'OUTPUT_LEN': 336,
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'TRAIN_VAL_TEST_RATIO': [0.7, 0.1, 0.2],
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'NORM_EACH_CHANNEL': False,
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'RESCALE': True,
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'METRICS': ['MAE', 'RMSE', 'MAPE'],
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'NULL_VAL': np.nan
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}
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def load_and_preprocess_data():
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'''Load and preprocess raw data, selecting the specified channel(s).'''
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data = np.load(data_file_path)
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data = data[..., target_channel]
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print(f'Raw time series shape: {data.shape}')
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return data
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def save_data(data):
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'''Save the preprocessed data to a binary file.'''
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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file_path = os.path.join(output_dir, 'data.dat')
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fp = np.memmap(file_path, dtype='float32', mode='w+', shape=data.shape)
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fp[:] = data[:]
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fp.flush()
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del fp
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print(f'Data saved to {file_path}')
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def save_description(data):
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'''Save a description of the dataset to a JSON file.'''
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description = {
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'name': dataset_name,
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'domain': domain,
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'shape': data.shape,
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'num_time_steps': data.shape[0],
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'num_nodes': data.shape[1],
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'num_features': data.shape[2],
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'feature_description': feature_description,
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'has_graph': graph_file_path is not None,
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'frequency (minutes)': frequency,
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'settings': regular_settings
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}
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description_path = os.path.join(output_dir, 'desc.json')
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with open(description_path, 'w') as f:
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json.dump(description, f, indent=4)
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print(f'Description saved to {description_path}')
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print(description)
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def main():
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# Load and preprocess data
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data = load_and_preprocess_data()
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# Save processed data
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save_data(data)
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# Save dataset description
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save_description(data)
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if __name__ == '__main__':
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main()
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