def generate_test_result(self,
test_collection: cri.CrCollection,
verbose=1,
save_to_instance_key=None,
exp_key=None,
verbose_short_name=None,
description='',
workers=4,
use_multiprocessing=False,
params=None) -> Result:
"""
Genereates a Result based on predictions against test_collection.
When save_to_instance_key is not None, the results are saved to
<model_key>/<save_to_instance_key>/cr_result.json
"""
model = self.get_model()
test_gen = self.get_test_generator(test_collection)
test_gen.reset()
if (verbose):
print('Generating predictions for {}'.format(
self.get_key()).center(80, '-'))
predictions = model.predict_generator(
test_gen,
steps=len(test_gen),
workers=workers,
use_multiprocessing=use_multiprocessing,
verbose=1)
cr_codes = cri.extract_cr_codes(test_gen.filenames)
if verbose_short_name is None:
short_name = self.get_key()
short_name += ' analyzed on {}'.format(
datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
else:
short_name = verbose_short_name
if params is None:
params = dict()
result = Result.from_predictions(predictions, cr_codes, params,
short_name, description)
if save_to_instance_key:
result.save(self.get_key(), save_to_instance_key, exp_key)
return result
