| import numpy as np
|
| import pandas as pd
|
|
|
| def load_ATFM(dset_name, mode, path):
|
| """
|
| Loads the dataset from TSV files, handling NaN values, and returns the data and labels.
|
|
|
| Parameters:
|
| - dset_name: String, the base name for the TSV files
|
| - mode: String, typically 'TRAIN' or 'TEST'
|
| - path: String, the directory path where files are stored
|
|
|
| Returns:
|
| - data: Numpy array of shape (N, T, 3), with NaN values preserved
|
| - labels: Numpy array of shape (N,)
|
| """
|
| variables = ['X', 'Y', 'Z']
|
| data = []
|
| labels = None
|
|
|
| for var in variables:
|
| tsv_filename = f'{path}/{dset_name}_{mode}_{var}.tsv'
|
| df = pd.read_csv(tsv_filename, sep='\t', header=None, na_values='NaN')
|
| if labels is None:
|
| labels = df.values[:, 0]
|
| var_data = df.values[:, 1:]
|
| data.append(var_data)
|
|
|
| data = np.stack(data, axis=-1)
|
|
|
| return data, labels.astype(int)
|
|
|
|
|
| train_data, train_labels = load_ATFM('RKSIa', 'TRAIN', 'RKSIa')
|
| test_data, test_labels = load_ATFM('RKSIa', 'TEST', 'RKSIa')
|
|
|
|
|
| print(train_data.shape, train_labels.shape)
|
| print(test_data.shape, test_labels.shape) |
