from pathlib2 import Path
import scipy.io as sio
import numpy as np
from deep_bottleneck import utils
from deep_bottleneck.datasets.base_dataset import Dataset
[docs]def load(nb_dir='') -> Dataset:
"""Load the Information Bottleneck harmonics dataset
Returns:
The harmonics dataset.
"""
ID = '2017_12_21_16_51_3_275766'
n_classes = 2
data_file = Path(nb_dir + 'datasets/IB_data_' + str(ID) + '.npz')
if not data_file.is_file():
import_IB_data_from_mat(ID, nb_dir)
data = np.load(nb_dir + 'datasets/IB_data_' + str(ID) + '.npz')
X_train = data['X_train']
y_train = data['y_train']
X_test = data['X_test']
y_test = data['y_test']
dataset = Dataset.from_labelled_subset(X_train, y_train, X_test, y_test, n_classes)
return dataset
[docs]def import_IB_data_from_mat(name_ID, nb_dir=''):
""" Writes a .npy file to disk containing the harmonics dataset used by Tishby
Args:
name_ID: Identifier which is going to be part of the output filename
Returns:
None
"""
print('Loading Data...')
d = sio.loadmat(nb_dir + 'datasets/var_u.mat')
F = d['F']
y = d['y']
C = type('type_C', (object,), {})
data_sets_original = C()
data_sets_original.data = F
data_sets_original.labels = np.squeeze(np.concatenate((y[None, :], 1 - y[None, :]), axis=0).T)
data_sets = utils.data_shuffle(data_sets_original, 80, shuffle_data=True)
X_train, y_train, X_test, y_test = data_sets.train.data, data_sets.train.labels[:,
0], data_sets.test.data, data_sets.test.labels[:, 0]
np.savez_compressed(nb_dir + 'datasets/IB_data_' + str(name_ID), X_train=X_train, y_train=y_train, X_test=X_test,
y_test=y_test)