def test_model_can_be_read(self):
'''
Check whether a model can be read
'''
output_model = random.choice(self.settings['SUBJECTS'])
parsed_model = utils.read_trained_model(output_model, self.settings)
self.assertIsInstance(parsed_model,
sklearn.pipeline.Pipeline,
msg="Check that randomly picked model ({0}) is "
"the correct sklearn obj type".format(output_model))
def test_model_can_be_read(self):
'''
Check whether a model can be read
'''
output_model = random.choice(self.settings['SUBJECTS'])
parsed_model = utils.read_trained_model(output_model, self.settings)
self.assertIsInstance(parsed_model,
sklearn.pipeline.Pipeline,
msg="Check that randomly picked model ({0}) is "
"the correct sklearn obj type".format(output_model))
def main(settings_file='SETTINGS.json', verbose=False):
# load the settings
settings = utils.get_settings(settings_file)
subjects = settings['SUBJECTS']
features = settings['FEATURES']
# load the data
data = utils.get_data(settings)
# load the metadata
metadata = utils.get_metadata()
features_that_parsed = list(data.keys())
settings['FEATURES'] = [
feature for feature in settings['FEATURES']
if feature in features_that_parsed
]
# check if features are 1-minute
if all('10feat' in feature for feature in settings['FEATURES']):
# set the flage
minutefeatures = True
elif not all('10feat' in feature for feature in settings['FEATURES']) and \
any('10feat' in feature for feature in settings['FEATURES']):
raise ValueError("Cannot mix 1-minute and 10-minute features.")
else:
minutefeatures = False
# iterate over subjects
prediction_dict = {}
for subject in subjects:
# load the trained model:
model = utils.read_trained_model(subject, settings, verbose=verbose)
# initialise the data assembler
assembler = utils.DataAssembler(settings, data, metadata)
# build test set
X = assembler.build_test(subject)
# make predictions
predictions = model.predict_proba(X)
# if using minute features combine the estimates
# on each segment by averaging
if minutefeatures:
segmentdict = {}
for segment, prediction in zip(assembler.test_segments,
predictions):
if segment not in segmentdict:
segmentdict[segment] = []
segmentdict[segment].append(prediction)
# gathered all predictions corresponding to a segment together
# now average them along their columns
for segment in assembler.test_segments:
segmentdict[segment] = np.vstack(segmentdict[segment])
segmentdict[segment] = np.mean(segmentdict[segment], axis=0)
for segment, prediction in zip(assembler.test_segments, predictions):
prediction_dict[segment] = prediction
utils.output_csv(prediction_dict, settings, verbose=verbose)
