def save_model(self, model):
"""
Save the dictionary to hdf5 file
"""
se_model = jsonpickler.dump(model)
print(se_model)
h5file = h5py.File(self.model_file, 'w')
def recursive_save_model(h5file_obj, dictionary):
for model_key, model_value in dictionary.items():
type_name = type(model_value).__name__
try:
if type_name in ['ndarray']:
h5file_obj.create_dataset(model_key,
(model_value.shape),
data=model_value)
elif type_name in ['list']:
if len(model_value) > 0:
list_obj = all(
isinstance(x, dict) for x in model_value)
if list_obj is False:
h5file_obj.create_dataset(
model_key, data=json.dumps(model_value))
else:
for index, model_item in enumerate(
model_value):
model_key_item = model_key + "/" + str(
index)
if model_item is not None:
if model_key_item in h5file_obj:
recursive_save_model(
model_key_item, model_item)
else:
group = h5file_obj.create_group(
model_key_item)
recursive_save_model(
group, model_item)
else:
h5file_obj.create_dataset(
model_key_item,
data=json.dumps(model_item))
else:
h5file_obj.create_dataset(model_key,
data=model_value)
elif type_name in [
'int', 'int32', 'int64', 'float', 'float32',
'float64', 'str', 'tuple', 'bool', 'None',
'NoneType'
]:
if type_name in ['None', 'NoneType']:
h5file_obj.create_dataset(
model_key, data=json.dumps(model_value))
else:
h5file_obj.create_dataset(model_key,
data=model_value)
elif type_name in ['dict']:
if model_key in h5file_obj:
recursive_save_model(h5file_obj[model_key],
model_value)
else:
group = h5file_obj.create_group(model_key)
recursive_save_model(group, model_value)
except Exception as exp:
print(model_key, exp, model_value)
continue
recursive_save_model(h5file, se_model)
print("--------------")
try:
key_value = h5file_obj.get(key).value
model_obj[key] = json.loads(key_value)
except Exception as exp:
if type(key_value).__name__ in ['ndarray']:
model_obj[key] = key_value.tolist()
else:
model_obj[key] = key_value
continue
return model_obj
reconstructed_model = recursive_load_model(h5file, model_obj)
print(reconstructed_model)
unloaded_model = jsonpickler.load(reconstructed_model)
return unloaded_model'''
if __name__ == "__main__":
if len(sys.argv) != 1:
print("Usage: python todictrecurr.py")
exit(1)
start_time = time.time()
serialize_clf = SerializeClass()
X_test, y_test, classifier = serialize_clf.serialize_class()
se_classifier = jsonpickler.dump(classifier)
deserialize = DeserializeClass(serialize_clf.model_file)
de_classifier = deserialize.load_model()
#serialize_clf.compute_prediction_score(de_classifier, X_test, y_test)
end_time = time.time()
print("Program finished in %s seconds" % str(end_time - start_time))