pb.py

import os
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import GeneralizingEstimator

# Need cluster_level, time_delaying_ridge

# All the functions that currently use ProgressBar
try:
    os.remove('/tmp/null.part')
except Exception:
    pass
# mne.utils._fetch_file('https://github.com/mne-tools/mne-python/archive/v0.19.1.zip', '/tmp/null', verbose=True)

data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
events_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
mne.preprocessing.mark_flat(raw, verbose=True)
picks = mne.pick_types(raw.info, meg=True, exclude='bads')  # Pick MEG channels
events = mne.read_events(events_fname)
event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
            'Visual/Left': 3, 'Visual/Right': 4}
epochs = mne.Epochs(raw, events, event_id, picks=picks, preload=True)
clf = make_pipeline(StandardScaler(), LogisticRegression(solver='lbfgs'))
time_gen = GeneralizingEstimator(clf, scoring='roc_auc', n_jobs=2,
                                 verbose=True)
time_gen.fit(X=epochs['Left'].get_data(),
             y=epochs['Left'].events[:, 2] > 2)
scores = time_gen.score(X=epochs['Right'].get_data(),
                        y=epochs['Right'].events[:, 2] > 2)