logger.info('\tWorking on Adi ID:\t%s' % adi_id)
# Get the indices of the samples with the target Adi ID
tar_idxs = np.array(
[this_adi_id in [adi_id] for this_adi_id in adi_ids])
# Use the samples with this adipose ID as the test set,
# all others assigned to train set
test_data = g2_d[tar_idxs, :, :]
train_data = g2_d[~tar_idxs, :, :]
test_md = g2_md[tar_idxs]
train_md = g2_md[~tar_idxs]
# Perform data augmentation on the training set here
train_data, train_md = full_aug(train_data, train_md)
# Get class labels for train/test sets here
test_labels = get_class_labels(test_md)
train_labels = get_class_labels(train_md)
test_labels = to_categorical(test_labels)
train_labels = to_categorical(train_labels)
# Resize data for use with keras
test_data = resize_features_for_keras(test_data)
train_data = resize_features_for_keras(train_data)
# Init arrays for storing results for this test set
aucs_here = np.zeros([
__N_RUNS,
])
g2_d = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_fd.pickle'))
g2_md = load_pickle(os.path.join(__DATA_DIR, 'g2/g2_metadata.pickle'))
# Load the training data and metadata from Gen-1
g1_d = load_pickle(os.path.join(__DATA_DIR,
'g1-train-test/test_fd.pickle'))
g1_md = load_pickle(
os.path.join(__DATA_DIR, 'g1-train-test/test_md.pickle'))
# Convert data to time domain, take magnitude, apply window
g1_d = correct_g1_ini_ant_ang(g1_d)
g1_d = np.abs(to_td(g1_d))
g2_d = np.abs(to_td(g2_d))
# Perform data augmentation
g2_d, g2_md = full_aug(g2_d, g2_md)
g2_d = resize_features_for_keras(g2_d)
g1_d = resize_features_for_keras(g1_d)
g2_labels = to_categorical(get_class_labels(g2_md))
g1_labels = to_categorical(get_class_labels(g1_md))
n_runs = 20
# Init arrays for storing performance metrics
auc_scores = np.zeros([
n_runs,
])
accs = np.zeros([
n_runs,
])