def test_detectors_missing(self):
with self.assertRaises(builders.InsufficientConfiguration):
builders.evaluator_from_dict({
"input_dir": "data",
"output_dir": "generated",
"records": []
})
def setUp(self):
path = '/'.join([THIS_DIR, 'configurations', 'do_everything.json'])
with open(path) as f:
self.conf = json.load(f)
self.conf["input_dir"] = absify(self.conf["input_dir"],
dirname(f.name))
self.conf["output_dir"] = absify(self.conf["output_dir"],
dirname(f.name))
self.evaluator = evaluator_from_dict(self.conf)
def test_basic_configuration(self):
evaluator = builders.evaluator_from_dict({
"input_dir":
"data",
"output_dir":
"generated",
"detectors": [{
"type": "WfdbXQRSDetector"
}],
"records": []
})
self.assertEqual(evaluator.input_dir, "data")
self.assertEqual(evaluator.output_dir, "generated")
self.assertEqual(evaluator.detectors[0].__class__, WfdbXQRSDetector)
self.assertEqual(len(evaluator.records), 0)
def test_name_builder_injection(self):
name_builder = builders.NameBuilder()
conf_dict = {
"input_dir": "data",
"output_dir": "generated",
"detectors": [{
"type": "WfdbXQRSDetector"
}],
"records": []
}
evaluator = builders.evaluator_from_dict(conf_dict, name_builder)
self.assertEqual(name_builder.already_in_use[0],
evaluator.detectors[0].name)
self.assertEqual(len(name_builder.already_in_use), 1)
def test_output_dir_missing(self):
with self.assertRaises(builders.InsufficientConfiguration):
builders.evaluator_from_dict({"input_dir": "data"})
help="Configuration file specifying the evaluation.")
args = parser.parse_args()
sys.path.append(join(dirname(__file__), "./raccoon"))
from raccoon.utils.builders import evaluator_from_dict, NameBuilder
for configuration in args.configurations:
conf = load_configuration(configuration)
if not conf.verbose:
# disable Tensorflow logging
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# disable Python warnings
if not sys.warnoptions:
warnings.simplefilter("ignore")
name_builder = NameBuilder()
evaluator = evaluator_from_dict(conf._asdict(), name_builder)
if conf.cv_method == "loocv":
evaluator.loocv()
elif conf.cv_method == "k2":
evaluator.kfold(k=2)
elif conf.cv_method == "k10":
evaluator.kfold(k=10)
elif conf.cv_method == 'defined':
evaluator.defined(conf.test_records)