def test_trainer_ba(self):
"""Test the `data-batch` subcommands."""
with db.session_scope(META_FILE) as (session, metadata):
cache = PhenotypeCache()
cache.make(IMAGE_DIR, TEMP_DIR, self.config, update=False)
train_data = BatchMakeTrainData(self.config, TEMP_DIR)
train_data.batch_export(self.train_dir)
def test_trainer_ab(self):
"""Test the `data` subcommands."""
filter_ = self.config.classification.filter.as_dict()
with db.session_scope(META_FILE) as (session, metadata):
cache = PhenotypeCache()
cache.make(IMAGE_DIR, TEMP_DIR, self.config, update=False)
train_data = MakeTrainData(self.config, TEMP_DIR)
train_data.export(self.train_file, filter_)
def validate(config, k, autoskip=False):
"""Start validation routines."""
with db.session_scope(META_FILE) as (session, metadata):
cache = PhenotypeCache()
cache.make(IMAGE_DIR, TEMP_DIR, config, update=False)
validator = Validator(config, TEMP_DIR, TEMP_DIR)
scores = validator.k_fold_xval_stratified(k, autoskip)
print
for path in sorted(scores.keys()):
values = np.array(scores[path])
print "Accuracy[{path}]: {mean:.2%} (+/- {sd:.2%})".format(**{
'path': path,
'mean': values.mean(),
'sd': values.std() * 2
})
