if isinstance(config['pipeline'], BaseEstimator):
pipeline = deepcopy(config['pipeline'])
else:
log.error(config['pipeline'])
raise (
ValueError('pipeline must be a list or a sklearn estimator'))
# append the pipeline in the paradigm list
if paradigm not in paradigms.keys():
paradigms[paradigm] = {}
# FIXME name are not unique
log.debug('Pipeline: \n\n {} \n'.format(get_string_rep(pipeline)))
paradigms[paradigm][config['name']] = pipeline
all_results = []
for paradigm in paradigms:
# get the context
if len(context_params) == 0:
context_params[paradigm] = {}
log.debug('{}: {}'.format(paradigm, context_params[paradigm]))
p = getattr(moabb_paradigms, paradigm)(**context_params[paradigm])
context = WithinSessionEvaluation(paradigm=p,
random_state=42,
n_jobs=options.threads)
results = context.process(pipelines=paradigms[paradigm])
all_results.append(results)
analyze(pd.concat(all_results, ignore_index=True),
options.output,
plot=options.plot)
import logging
import coloredlogs
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
coloredlogs.install(level=logging.DEBUG)
datasets = utils.dataset_search('imagery',
events=['supination', 'hand_close'],
has_all_events=False,
min_subjects=2,
multi_session=False)
for d in datasets:
d.subject_list = d.subject_list[:10]
paradigm = ImageryNClass(2)
context = WithinSessionEvaluation(paradigm=paradigm,
datasets=datasets,
random_state=42)
pipelines = OrderedDict()
pipelines['av+TS'] = make_pipeline(Covariances(estimator='oas'),
TSclassifier())
pipelines['av+CSP+LDA'] = make_pipeline(Covariances(estimator='oas'), CSP(8),
LDA())
results = context.process(pipelines, overwrite=True)
analyze(results, './')