from simdeep.config import PATH_TO_SAVE_MODEL
from os.path import isfile
from os.path import isdir
from os import mkdir
# from sys import version_info
# if version_info > (3, 0, 0):
# import pickle as cPickle
# else:
# import cPickle
import dill
from time import time
[docs]def save_model(boosting, path_to_save_model=PATH_TO_SAVE_MODEL):
""" """
if not isdir(path_to_save_model):
mkdir(path_to_save_model)
boosting._convert_logs()
t = time()
with open('{0}/{1}.pickle'.format(
path_to_save_model,
boosting._project_name), 'wb') as f_pick:
dill.dump(boosting, f_pick)
print('model saved in %2.1f s at %s/%s.pickle' % (
time() - t, path_to_save_model, boosting._project_name))
[docs]def load_model(project_name, path_model=PATH_TO_SAVE_MODEL):
""" """
t = time()
project_name = project_name.replace('.pickle', '') + '.pickle'
assert(isfile('{0}/{1}'.format(path_model, project_name)))
with open('{0}/{1}'.format(path_model, project_name), 'rb') as f_pick:
boosting = dill.load(f_pick)
print('model loaded in %2.1f s' % (time() - t))
return boosting
[docs]def feature_selection_usage_type(value):
""" """
if value not in {'individual',
'lasso',
None}:
raise Exception(
"feature_selection_usage_type: {0} should be from the following choices:" \
" ['individual', 'lasso', None]" \
.format(value))
return value
[docs]def load_labels_file(path_labels, sep="\t"):
"""
"""
labels_dict = {}
for line in open(path_labels):
split = line.strip().split(sep)
if len(split) < 2:
raise Exception(
'## Errorfor file in load_labels_file: {0} for line{1}' \
' line cannot be splitted in more than 2'.format(
line, path_labels))
patient, label = split[0], split[1]
try:
label = int(float(label))
except Exception:
raise Exception(
'## Error: in load_labels_file {0} for line {1}' \
'labels should be an int'.format(
path_labels, line))
if len(split) > 2:
try:
proba = float(split[2])
except Exception:
raise Exception(
'## Error: in load_labels_file {0} for line {1}' \
'label proba in column 3 should be a float'.format(
path_labels, line))
else:
proba = label
labels_dict[patient] = (label, proba)
return labels_dict