# Create the training data and label
training_data = [
arr for idx_arr, arr in enumerate(data) if idx_arr != idx_lopo_cv
]
training_label = [
arr for idx_arr, arr in enumerate(label) if idx_arr != idx_lopo_cv
]
# Concatenate the data
training_data = np.atleast_2d(np.hstack(training_data)).T
training_data = np.nan_to_num(training_data)
training_label = label_binarize(
np.hstack(training_label).astype(int), [0, 255])
print 'Create the training set ...'
# Perform the classification for the current cv and the
# given configuration
result_cv.append(
Classify(training_data, training_label, testing_data,
testing_label, **c))
# Concatenate the results per configuration
result_config.append(result_cv)
# Save the information
path_store = '/data/prostate/results/lemaitre-2016-nov/semi-washin-normalized'
if not os.path.exists(path_store):
os.makedirs(path_store)
joblib.dump(result_config,
os.path.join(path_store, 'results_normalized_ese.pkl'))
testing_data = np.atleast_2d(data[idx_lopo_cv]).T
testing_label = label_binarize(label[idx_lopo_cv], [0, 255])
print 'Create the testing set ...'
# Create the training data and label
training_data = [arr for idx_arr, arr in enumerate(data)
if idx_arr != idx_lopo_cv]
training_label = [arr for idx_arr, arr in enumerate(label)
if idx_arr != idx_lopo_cv]
# Concatenate the data
training_data = np.atleast_2d(np.hstack(training_data)).T
training_label = label_binarize(np.hstack(training_label).astype(int),
[0, 255])
print 'Create the training set ...'
# Perform the classification for the current cv and the
# given configuration
result_cv.append(Classify(training_data, training_label,
testing_data, testing_label,
**c))
# Concatenate the results per configuration
result_config.append(result_cv)
# Save the information
path_store = '/data/prostate/results/lemaitre-2016-nov/pun-r'
if not os.path.exists(path_store):
os.makedirs(path_store)
joblib.dump(result_config, os.path.join(path_store,
'results_normalized_ese.pkl'))
