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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] # Assumes labels are the first column and only need to be read once
var_data = df.values[:, 1:]
data.append(var_data)
data = np.stack(data, axis=-1)
return data, labels.astype(int)
# Example usage
train_data, train_labels = load_ATFM('RKSIa', 'TRAIN', 'RKSIa')
test_data, test_labels = load_ATFM('RKSIa', 'TEST', 'RKSIa')
# Print the shapes of the loaded data
print(train_data.shape, train_labels.shape)
print(test_data.shape, test_labels.shape) |