def test_MDM_transform():
"""Test transform of MDM"""
covset = generate_cov(100,3)
labels = np.array([0,1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset,labels)
mdm.transform(covset)
def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.predict(covset)
def test_MDM_transform():
"""Test transform of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.transform(covset)
def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100,3)
labels = np.array([0,1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset,labels)
mdm.predict(covset)
def check_other_classifiers(train_X, train_y, test_X, test_y):
from pyriemann.classification import MDM, TSclassifier
from sklearn.linear_model import LogisticRegression
from pyriemann.estimation import Covariances
from sklearn.pipeline import Pipeline
from mne.decoding import CSP
import seaborn as sns
import pandas as pd
train_y = [np.where(i == 1)[0][0] for i in train_y]
test_y = [np.where(i == 1)[0][0] for i in test_y]
cov_data_train = Covariances().transform(train_X)
cov_data_test = Covariances().transform(test_X)
cv = KFold(n_splits=10, random_state=42)
clf = TSclassifier()
scores = cross_val_score(clf, cov_data_train, train_y, cv=cv, n_jobs=1)
print("Tangent space Classification accuracy: ", np.mean(scores))
clf = TSclassifier()
clf.fit(cov_data_train, train_y)
print(clf.score(cov_data_test, test_y))
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
scores = cross_val_score(mdm, cov_data_train, train_y, cv=cv, n_jobs=1)
print("MDM Classification accuracy: ", np.mean(scores))
mdm = MDM()
mdm.fit(cov_data_train, train_y)
fig, axes = plt.subplots(1, 2)
ch_names = [ch for ch in range(8)]
df = pd.DataFrame(data=mdm.covmeans_[0], index=ch_names, columns=ch_names)
g = sns.heatmap(df,
ax=axes[0],
square=True,
cbar=False,
xticklabels=2,
yticklabels=2)
g.set_title('Mean covariance - feet')
df = pd.DataFrame(data=mdm.covmeans_[1], index=ch_names, columns=ch_names)
g = sns.heatmap(df,
ax=axes[1],
square=True,
cbar=False,
xticklabels=2,
yticklabels=2)
plt.xticks(rotation='vertical')
plt.yticks(rotation='horizontal')
g.set_title('Mean covariance - hands')
# dirty fix
plt.sca(axes[0])
plt.xticks(rotation='vertical')
plt.yticks(rotation='horizontal')
plt.savefig("meancovmat.png")
plt.show()
def create_mdm(raw, event_id):
tmin, tmax = -1., 4.
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=None, preload=True, verbose=False)
labels = epochs.events[:, -1]
epochs_data_train = epochs.get_data()[:, :-1]
cov_data_train = Covariances().transform(epochs_data_train)
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(cov_data_train, labels)
return mdm
def erp_cov_vr_pc(X_training, labels_training, X_test, labels_test, class_name,
class_info):
# estimate the extended ERP covariance matrices with Xdawn
erpc = ERPCovariances(classes=[class_info[class_name]], estimator='lwf')
erpc.fit(X_training, labels_training)
covs_training = erpc.transform(X_training)
covs_test = erpc.transform(X_test)
# get the AUC for the classification
clf = MDM()
clf.fit(covs_training, labels_training)
labels_pred = clf.predict(covs_test)
return roc_auc_score(labels_test, labels_pred)
class FgMDM2(BaseEstimator, ClassifierMixin, TransformerMixin):
def __init__(self, metric='riemann', tsupdate=False, n_jobs=1):
"""Init."""
self.metric = metric
self.n_jobs = n_jobs
self.tsupdate = tsupdate
if isinstance(metric, str):
self.metric_mean = metric
elif isinstance(metric, dict):
# check keys
for key in ['mean', 'distance']:
if key not in metric.keys():
raise KeyError('metric must contain "mean" and "distance"')
self.metric_mean = metric['mean']
else:
raise TypeError('metric must be dict or str')
def fit(self, X, y):
self.classes_ = unique_labels(y)
self._mdm = MDM(metric=self.metric, n_jobs=self.n_jobs)
self._fgda = FGDA(metric=self.metric_mean, tsupdate=self.tsupdate)
cov = self._fgda.fit_transform(X, y)
self._mdm.fit(cov, y)
return self
def predict(self, X):
cov = self._fgda.transform(X)
return self._mdm.predict(cov)
def predict_proba(self, X):
cov = self._fgda.transform(X)
return self._mdm.predict_proba(cov)
def transform(self, X):
cov = self._fgda.transform(X)
return self._mdm.transform(cov)
def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.predict(covset)
# test fit_predict
mdm = MDM(metric='riemann')
mdm.fit_predict(covset, labels)
# test transform
mdm.transform(covset)
# predict proba
mdm.predict_proba(covset)
# test n_jobs
mdm = MDM(metric='riemann', n_jobs=2)
mdm.fit(covset, labels)
mdm.predict(covset)
def test_MDM_predict():
"""Test prediction of MDM"""
covset = generate_cov(100, 3)
labels = np.array([0, 1]).repeat(50)
mdm = MDM(metric='riemann')
mdm.fit(covset, labels)
mdm.predict(covset)
# test fit_predict
mdm = MDM(metric='riemann')
mdm.fit_predict(covset, labels)
# test transform
mdm.transform(covset)
# predict proba
mdm.predict_proba(covset)
# test n_jobs
mdm = MDM(metric='riemann', n_jobs=2)
mdm.fit(covset, labels)
mdm.predict(covset)
class DistanceCalculatorAlex(BaseEstimator, TransformerMixin):
"""Distance Calulator Based on MDM."""
def __init__(self,
metric_mean='logeuclid',
metric_dist=['riemann'],
n_jobs=7,
subsample=10):
"""Init."""
self.metric_mean = metric_mean
self.metric_dist = metric_dist
self.n_jobs = n_jobs
self.subsample = subsample
def fit(self, X, y):
"""Fit."""
self.mdm = MDM(metric=self.metric_mean, n_jobs=self.n_jobs)
labels = np.squeeze(create_sequence(y.T)[::self.subsample])
self.mdm.fit(X, labels)
return self
def transform(self, X, y=None):
"""Transform."""
feattr = []
for metric in self.metric_dist:
self.mdm.metric_dist = metric
feat = self.mdm.transform(X)
# substract distance of the class 0
feat = feat[:, 1:] - np.atleast_2d(feat[:, 0]).T
feattr.append(feat)
feattr = np.concatenate(feattr, axis=1)
feattr[np.isnan(feattr)] = 0
return feattr
def fit_transform(self, X, y):
"""Fit and transform."""
self.fit(X, y)
return self.transform(X)
class DistanceCalculatorAlex(BaseEstimator, TransformerMixin):
"""Distance Calulator Based on MDM."""
def __init__(self, metric_mean='logeuclid', metric_dist=['riemann'],
n_jobs=7, subsample=10):
"""Init."""
self.metric_mean = metric_mean
self.metric_dist = metric_dist
self.n_jobs = n_jobs
self.subsample = subsample
def fit(self, X, y):
"""Fit."""
self.mdm = MDM(metric=self.metric_mean, n_jobs=self.n_jobs)
labels = np.squeeze(create_sequence(y.T)[::self.subsample])
self.mdm.fit(X, labels)
return self
def transform(self, X, y=None):
"""Transform."""
feattr = []
for metric in self.metric_dist:
self.mdm.metric_dist = metric
feat = self.mdm.transform(X)
# substract distance of the class 0
feat = feat[:, 1:] - np.atleast_2d(feat[:, 0]).T
feattr.append(feat)
feattr = np.concatenate(feattr, axis=1)
feattr[np.isnan(feattr)] = 0
return feattr
def fit_transform(self, X, y):
"""Fit and transform."""
self.fit(X, y)
return self.transform(X)
class wrapper_MDM(machine_learning_method):
"""wrapper for pyriemann MDM"""
def __init__(self, method_name, method_args):
super(wrapper_MDM, self).__init__(method_name, method_args)
self.init_method()
def init_method(self, n_jobs=1):
self.classifier = MDM(metric=self.method_args['metric'], n_jobs=n_jobs)
def set_parallel(self, is_parallel=False, n_jobs=8):
logging.warning(
'The call to this set_parallel method is reseting the class, and must be fitted again'
)
self.parallel = is_parallel
self.n_jobs = n_jobs
if self.parallel:
self.init_method(n_jobs)
def fit(self, X, y):
return self.classifier.fit(X, y)
def predict(self, X):
return self.classifier.predict(X)
csp = CSP(n_components=4, reg='ledoit_wolf', log=True)
clf = Pipeline([('CSP', csp), ('LogisticRegression', lr)])
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("CSP + LDA Classification accuracy: %f / Chance level: %f" %
(np.mean(scores), class_balance))
###############################################################################
# Display MDM centroid
mdm = MDM()
mdm.fit(cov_data_train, labels)
fig, axes = plt.subplots(1, 2, figsize=[8, 4])
ch_names = [ch.replace('.', '') for ch in epochs.ch_names]
df = pd.DataFrame(data=mdm.covmeans_[0], index=ch_names, columns=ch_names)
g = sns.heatmap(df,
ax=axes[0],
square=True,
cbar=False,
xticklabels=2,
yticklabels=2)
g.set_title('Mean covariance - hands')
df = pd.DataFrame(data=mdm.covmeans_[1], index=ch_names, columns=ch_names)
g = sns.heatmap(df,
epochs = make_fixed_length_epochs(
raw_ext, duration=duration, overlap=duration - interval, verbose=False)
x_test = BlockCovariances(
estimator='lwf', block_size=ch_count).transform(epochs.get_data())
###############################################################################
# Classification with minimum distance to mean (MDM)
# --------------------------------------------------
#
# Classification for a 4-class SSVEP BCI, including resting-state class.
print("Number of training trials: {}".format(len(x_train)))
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(x_train, y_train)
###############################################################################
# Projection in tangent space with principal geodesic analysis (PGA)
# ------------------------------------------------------------------
#
# Project covariance matrices from the Riemannian manifold into the Euclidean
# tangent space at the grand average, and apply a principal component analysis
# (PCA) to obtain an unsupervised dimension reduction [1]_.
pga = make_pipeline(
TangentSpace(metric="riemann", tsupdate=False),
PCA(n_components=2)
)
def fit(self, X, y):
# validate
X, y = check_X_y(X, y, allow_nd=True)
X = check_array(X, allow_nd=True)
# set internal vars
self.classes_ = unique_labels(y)
self.X_ = X
self.y_ = y
##################################################
# split X into train and test sets, so that
# grid search can be performed on train set only
seed = 7
np.random.seed(seed)
#X_TRAIN, X_TEST, y_TRAIN, y_TEST = train_test_split(X, y, test_size=0.25, random_state=seed)
for epoch_trim in self.epoch_bounds:
for bandpass in self.bandpass_filters:
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=seed)
# X_train = np.copy(X_TRAIN)
# X_test = np.copy(X_TEST)
# y_train = np.copy(y_TRAIN)
# y_test = np.copy(y_TEST)
# separate out inputs that are tuples
bandpass_start, bandpass_end = bandpass
epoch_trim_start, epoch_trim_end = epoch_trim
# bandpass filter coefficients
b, a = butter(
5,
np.array([bandpass_start, bandpass_end]) /
(self.sfreq * 0.5), 'bandpass')
# filter and crop TRAINING SET
X_train = self.preprocess_X(X_train, b, a, epoch_trim_start,
epoch_trim_end)
# validate
X_train, y_train = check_X_y(X_train, y_train, allow_nd=True)
X_train = check_array(X_train, allow_nd=True)
# filter and crop TEST SET
X_test = self.preprocess_X(X_test, b, a, epoch_trim_start,
epoch_trim_end)
# validate
X_test, y_test = check_X_y(X_test, y_test, allow_nd=True)
X_test = check_array(X_test, allow_nd=True)
###########################################################################
# self-tune CSP to find optimal number of filters to use at these settings
#[best_num_filters, best_num_filters_score] = self.self_tune(X_train, y_train)
best_num_filters = 5
# as an option, we could tune optimal CSP filter num against complete train set
#X_tune = self.preprocess_X(X, b, a, epoch_trim_start, epoch_trim_end)
#[best_num_filters, best_num_filters_score] = self.self_tune(X_tune, y)
# now use this insight to really fit with optimal CSP spatial filters
"""
reg : float | str | None (default None)
if not None, allow regularization for covariance estimation
if float, shrinkage covariance is used (0 <= shrinkage <= 1).
if str, optimal shrinkage using Ledoit-Wolf Shrinkage ('ledoit_wolf')
or Oracle Approximating Shrinkage ('oas').
"""
transformer = CSP(n_components=best_num_filters,
reg='ledoit_wolf')
transformer.fit(X_train, y_train)
# use these CSP spatial filters to transform train and test
spatial_filters_train = transformer.transform(X_train)
spatial_filters_test = transformer.transform(X_test)
# put this back in as failsafe if NaN or inf starts cropping up
# spatial_filters_train = np.nan_to_num(spatial_filters_train)
# check_X_y(spatial_filters_train, y_train)
# spatial_filters_test = np.nan_to_num(spatial_filters_test)
# check_X_y(spatial_filters_test, y_test)
# train LDA
classifier = LinearDiscriminantAnalysis()
classifier.fit(spatial_filters_train, y_train)
score = classifier.score(spatial_filters_test, y_test)
#print "current score",score
print "bandpass:"******"epoch window:", epoch_trim_start, epoch_trim_end
#print best_num_filters,"filters chosen"
# put in ranked order Top 10 list
idx = bisect(self.ranked_scores, score)
self.ranked_scores.insert(idx, score)
self.ranked_scores_opts.insert(
idx,
dict(bandpass=bandpass,
epoch_trim=epoch_trim,
filters=best_num_filters))
self.ranked_classifiers.insert(idx, classifier)
self.ranked_transformers.insert(idx, transformer)
if len(self.ranked_scores) > self.num_votes:
self.ranked_scores.pop(0)
if len(self.ranked_scores_opts) > self.num_votes:
self.ranked_scores_opts.pop(0)
if len(self.ranked_classifiers) > self.num_votes:
self.ranked_classifiers.pop(0)
if len(self.ranked_transformers) > self.num_votes:
self.ranked_transformers.pop(0)
"""
Covariance computation
"""
# compute covariance matrices
cov_data_train = covariances(X=X_train)
cov_data_test = covariances(X=X_test)
clf_mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
clf_mdm.fit(cov_data_train, y_train)
score_mdm = clf_mdm.score(cov_data_test, y_test)
# print "MDM prediction score:",score_mdm
# put in ranked order Top 10 list
idx = bisect(self.ranked_scores_mdm, score_mdm)
self.ranked_scores_mdm.insert(idx, score_mdm)
self.ranked_scores_opts_mdm.insert(
idx,
dict(bandpass=bandpass,
epoch_trim=epoch_trim,
filters=best_num_filters))
self.ranked_classifiers_mdm.insert(idx, clf_mdm)
if len(self.ranked_scores_mdm) > self.num_votes:
self.ranked_scores_mdm.pop(0)
if len(self.ranked_scores_opts_mdm) > self.num_votes:
self.ranked_scores_opts_mdm.pop(0)
if len(self.ranked_classifiers_mdm) > self.num_votes:
self.ranked_classifiers_mdm.pop(0)
clf_ts = TSclassifier()
clf_ts.fit(cov_data_train, y_train)
score_ts = clf_ts.score(cov_data_test, y_test)
# put in ranked order Top 10 list
idx = bisect(self.ranked_scores_ts, score_ts)
self.ranked_scores_ts.insert(idx, score_ts)
self.ranked_scores_opts_ts.insert(
idx,
dict(bandpass=bandpass,
epoch_trim=epoch_trim,
filters=best_num_filters))
self.ranked_classifiers_ts.insert(idx, clf_ts)
if len(self.ranked_scores_ts) > self.num_votes:
self.ranked_scores_ts.pop(0)
if len(self.ranked_scores_opts_ts) > self.num_votes:
self.ranked_scores_opts_ts.pop(0)
if len(self.ranked_classifiers_ts) > self.num_votes:
self.ranked_classifiers_ts.pop(0)
print "CSP+LDA score:", score, "Tangent space w/LR score:", score_ts
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print "^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^"
print " T O P ", self.num_votes, " C L A S S I F I E R S"
print
#j=1
for i in xrange(len(self.ranked_scores)):
print i, ",", round(self.ranked_scores[i], 4), ",",
print self.ranked_scores_opts[i]
print "-------------------------------------"
for i in xrange(len(self.ranked_scores_ts)):
print i, ",", round(self.ranked_scores_ts[i], 4), ",",
print self.ranked_scores_opts_ts[i]
print "-------------------------------------"
for i in xrange(len(self.ranked_scores_mdm)):
print i, ",", round(self.ranked_scores_mdm[i], 4), ",",
print self.ranked_scores_opts_mdm[i]
# finish up, set the flag to indicate "fitted" state
self.fit_ = True
# Return the classifier
return self
def get_score(subject=7):
###############################################################################
# Set parameters and read data
# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1., 2.
event_id = dict(hands=2, feet=3)
runs = [6, 10, 14] # motor imagery: hands vs feet
raw_files = [
read_raw_edf(f, preload=True) for f in eegbci.load_data(subject, runs)
]
raw = concatenate_raws(raw_files)
picks = pick_types(raw.info,
meg=False,
eeg=True,
stim=False,
eog=False,
exclude='bads')
# subsample elecs
picks = picks[::2]
# Apply band-pass filter
raw.filter(7., 35., method='iir', picks=picks)
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
# Read epochs (train will be done only between 1 and 2s)
# Testing will be done with a running classifier
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
preload=True,
verbose=False)
labels = epochs.events[:, -1] - 2
# cross validation
cv = KFold(len(labels), 10, shuffle=True, random_state=42)
# get epochs
epochs_data_train = 1e6 * epochs.get_data()
# compute covariance matrices
cov_data_train = Covariances().transform(epochs_data_train)
###############################################################################
# Classification with Minimum distance to mean
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
# Use scikit-learn Pipeline with cross_val_score function
mdm.fit(cov_data_train, labels)
print(123)
###############################################################################
# Classification with Tangent Space Logistic Regression
clf = TSclassifier()
# Use scikit-learn Pipeline with cross_val_score function
scores = cross_val_score(clf, cov_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
ts_score = np.mean(scores)
print("Tangent space Classification accuracy: %f / Chance level: %f" %
(ts_score, class_balance))
###############################################################################
return [subject, mdm_score, ts_score]
def score_ensemble_rot(settings, subject_target, ntop):
dataset = settings['dataset']
paradigm = settings['paradigm']
session = settings['session']
storage = settings['storage']
filepath = '../results/' + dataset + '/TL_intra-subject_scores.pkl'
acc_intra_dict = joblib.load(filepath)
scores = []
subject_sources = []
for subject in settings['subject_list']:
if subject == subject_target:
continue
else:
scores.append(acc_intra_dict[subject])
subject_sources.append(subject)
scores = np.array(scores)
subject_sources = np.array(subject_sources)
idx_sort = scores.argsort()[::-1]
scores = scores[idx_sort]
subject_sources = subject_sources[idx_sort]
subject_sources_ntop = subject_sources[:ntop]
# get the geometric means for each subject (each class and also the center)
filename = '../results/' + dataset + '/subject_means.pkl'
subj_means = joblib.load(filename)
# get the data for the target subject
target_org = GD.get_dataset(dataset, subject_target, session, storage)
if paradigm == 'MI':
# things here are only implemented for MI for now
target_org['covs'] = Covariances(estimator='oas').fit_transform(
target_org['signals'])
target_org['labels'] = target_org['labels']
ncovs = settings['ncovs_list'][0]
nrzt = 10
score_rzt = 0.0
for rzt in range(nrzt):
# split randomly the target dataset
target_org_train, target_org_test = get_target_split_motorimagery(
target_org, ncovs)
covs_train_target = target_org_train['covs']
labs_train_target = target_org_train['labels']
MC_target = mean_riemann(covs_train_target)
M1_target = mean_riemann(
covs_train_target[labs_train_target == 'left_hand'])
M2_target = mean_riemann(
covs_train_target[labs_train_target == 'right_hand'])
M1_target_rct = np.dot(invsqrtm(MC_target),
np.dot(M1_target, invsqrtm(MC_target)))
M2_target_rct = np.dot(invsqrtm(MC_target),
np.dot(M2_target, invsqrtm(MC_target)))
covs_train_target = np.stack([M1_target_rct, M2_target_rct])
labs_train_target = np.array(['left_hand', 'right_hand'])
clf = []
for subj_source in subject_sources_ntop:
MC_source = subj_means[subj_source]['center']
M1_source = subj_means[subj_source]['left_hand']
M2_source = subj_means[subj_source]['right_hand']
M1_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M1_source, invsqrtm(MC_source)))
M2_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M2_source, invsqrtm(MC_source)))
M = [M1_target_rct, M2_target_rct]
Mtilde = [M1_source_rct, M2_source_rct]
R = manifoptim.get_rotation_matrix(M, Mtilde)
M1_source_rot = np.dot(R, np.dot(M1_source_rct, R.T))
M2_source_rot = np.dot(R, np.dot(M2_source_rct, R.T))
covs_train_source = np.stack([M1_source_rot, M2_source_rot])
labs_train_source = np.array(['left_hand', 'right_hand'])
covs_train = np.concatenate([covs_train_source, covs_train_target])
labs_train = np.concatenate([labs_train_source, labs_train_target])
clfi = MDM()
# problems here when using integer instead of floats on the sample_weight
clfi.fit(covs_train,
labs_train,
sample_weight=np.array(
[200.0, 200.0, 2.0 * ncovs, 2.0 * ncovs]))
clf.append(clfi)
covs_test = target_org_test['covs']
labs_test = target_org_test['labels']
ypred = []
for clfi in clf:
yi = clfi.predict(covs_test)
ypred.append(yi)
ypred = np.array(ypred)
majorvoting = []
for j in range(ypred.shape[1]):
ypredj = ypred[:, j]
values_unique, values_count = np.unique(ypredj, return_counts=True)
majorvoting.append(values_unique[np.argmax(values_count)])
majorvoting = np.array(majorvoting)
score_rzt = score_rzt + np.mean(majorvoting == labs_test)
score = score_rzt / nrzt
return score
def get_score(subject=7, runs=[6, 10, 14], event_id=dict(hands=2, feet=3)):
tmin, tmax = -1., 4.
# learn all suject exclude target subject. #############################
first_sub = 2 if subject == 1 else 1
raw = get_raw(first_sub, runs)
for i in range(first_sub + 1, 3):
if i != subject and not (i in [88, 89, 92, 100]):
# print(i)
raw.append(get_raw(i, runs))
raw.append(get_raw(subject, runs))
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
proj=True,
baseline=None,
preload=True,
verbose=False)
labels = epochs.events[:, -1]
epochs_data_train = 1e6 * epochs.get_data()[:, :-1]
cov_data_train = Covariances().transform(epochs_data_train)
weights = np.arange(0.1, 1.0, 0.1)
scores = []
for weight in weights:
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
others_sample_weight_base = np.ones(len(epochs) -
EPOCH_COUNT) * (1. - weight)
target_sample_weight_base = np.ones(EPOCH_COUNT) * weight
sample_weight = np.hstack(
(others_sample_weight_base, target_sample_weight_base))
others_size = others_sample_weight_base.size
others_index = np.arange(others_size)
cv = KFold(n_splits=5, shuffle=True, random_state=42)
train_scores = []
test_scores = []
dumy_array = np.ones(EPOCH_COUNT)
for train_index, test_index in cv.split(dumy_array):
train_index = np.hstack((others_index, train_index + others_size))
x = cov_data_train[train_index]
y = labels[train_index]
mdm.fit(x, y, sample_weight=sample_weight[train_index])
score = (mdm.predict(x) == y).sum() / len(train_index)
train_scores.append(score)
test_index = test_index + others_size
y = mdm.predict(cov_data_train[test_index])
score = (y == labels[test_index]).sum() / len(test_index)
test_scores.append(score)
train_score = np.mean(train_scores)
test_score = np.mean(test_scores)
scores.append([subject, weight, train_score, test_score])
# print("train:%s test:%s" % (train_score, test_score))
return scores
class ERPDistance(BaseEstimator, TransformerMixin):
"""ERP distance cov estimator.
This transformer estimates Riemannian distance for ERP covariance matrices.
After estimation of special form ERP covariance matrices using the ERP
transformer, a MDM [1] algorithm is used to compute Riemannian distance.
References:
[1] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass
Brain-Computer Interface Classification by Riemannian Geometry," in IEEE
Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012
"""
def __init__(self,
window=500,
nfilters=3,
subsample=1,
metric='riemann',
n_jobs=1):
"""Init."""
self.window = window
self.nfilters = nfilters
self.subsample = subsample
self.metric = metric
self.n_jobs = n_jobs
self._fitted = False
def fit(self, X, y):
"""fit."""
# Create ERP and get cov mat
self.ERP = ERP(self.window, self.nfilters, self.subsample)
train_cov = self.ERP.fit_transform(X, y)
labels_train = self.ERP.labels_train
# Add rest epochs
rest_cov = self._get_rest_cov(X, y)
train_cov = np.concatenate((train_cov, rest_cov), axis=0)
labels_train = np.concatenate((labels_train, [0] * len(rest_cov)))
# fit MDM
self.MDM = MDM(metric=self.metric, n_jobs=self.n_jobs)
self.MDM.fit(train_cov, labels_train)
self._fitted = True
return self
def transform(self, X, y=None):
"""Transform."""
test_cov = self.ERP.transform(X)
dist = self.MDM.transform(test_cov)
dist = dist[:, 1:] - np.atleast_2d(dist[:, 0]).T
return dist
def update_subsample(self, old_sub, new_sub):
"""update subsampling."""
if self._fitted:
self.ERP.update_subsample(old_sub, new_sub)
def _get_rest_cov(self, X, y):
"""Sample rest epochs from data and compute the cov mat."""
ix = np.where(np.diff(y[:, 0]) == 1)[0]
rest = []
offset = -self.window
for i in ix:
start = i + offset - self.window
stop = i + offset
rest.append(self.ERP.erp_cov(X[slice(start, stop)].T))
return np.array(rest)
blocks = np.arange(1, 12+1)
for train_idx, test_idx in kf.split(np.arange(12)):
# split in training and testing blocks
X_training, labels_training, _ = get_block_repetition(X, labels, meta, blocks[train_idx], repetitions)
X_test, labels_test, _ = get_block_repetition(X, labels, meta, blocks[test_idx], repetitions)
# estimate the extended ERP covariance matrices with Xdawn
dict_labels = {'Target':1, 'NonTarget':0}
erpc = ERPCovariances(classes=[dict_labels['Target']], estimator='lwf')
erpc.fit(X_training, labels_training)
covs_training = erpc.transform(X_training)
covs_test = erpc.transform(X_test)
# get the AUC for the classification
clf = MDM()
clf.fit(covs_training, labels_training)
labels_pred = clf.predict(covs_test)
auc.append(roc_auc_score(labels_test, labels_pred))
# stock scores
scores_subject.append(np.mean(auc))
scores.append(scores_subject)
# print results
df[tmax] = pd.DataFrame(scores, columns=['subject', 'VR', 'PC'])
filename = './results.pkl'
joblib.dump(df, filename)
def score_pooling_rct(settings, subject_target, ntop):
dataset = settings['dataset']
paradigm = settings['paradigm']
session = settings['session']
storage = settings['storage']
filepath = '../results/' + dataset + '/TL_intra-subject_scores.pkl'
acc_intra_dict = joblib.load(filepath)
scores = []
subject_sources = []
for subject in settings['subject_list']:
if subject == subject_target:
continue
else:
scores.append(acc_intra_dict[subject])
subject_sources.append(subject)
scores = np.array(scores)
subject_sources = np.array(subject_sources)
idx_sort = scores.argsort()[::-1]
scores = scores[idx_sort]
subject_sources = subject_sources[idx_sort]
subject_sources_ntop = subject_sources[:ntop]
# get the geometric means for each subject (each class and also the center)
filename = '../results/' + dataset + '/subject_means.pkl'
subj_means = joblib.load(filename)
# get the data for the target subject
target_org = GD.get_dataset(dataset, subject_target, session, storage)
if paradigm == 'MI':
# things here are only implemented for MI for now
target_org['covs'] = Covariances(estimator='oas').fit_transform(
target_org['signals'])
target_org['labels'] = target_org['labels']
ncovs = settings['ncovs_list'][0]
score_rzt = 0.0
nrzt = 10
for rzt in range(nrzt):
# split randomly the target dataset
target_org_train, target_org_test = get_target_split_motorimagery(
target_org, ncovs)
# get the data from the sources and pool it all together
class_mean_1 = []
class_mean_2 = []
for subj_source in subject_sources_ntop:
MC_source = subj_means[subj_source]['center']
M1_source = subj_means[subj_source]['left_hand']
M2_source = subj_means[subj_source]['right_hand']
M1_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M1_source, invsqrtm(MC_source)))
class_mean_1.append(M1_source_rct)
M2_source_rct = np.dot(invsqrtm(MC_source),
np.dot(M2_source, invsqrtm(MC_source)))
class_mean_2.append(M2_source_rct)
class_mean_1_source = np.stack(class_mean_1)
class_mean_2_source = np.stack(class_mean_2)
covs_train_source = np.concatenate(
[class_mean_1_source, class_mean_2_source])
labs_train_source = np.concatenate([
len(class_mean_1_source) * ['left_hand'],
len(class_mean_2_source) * ['right_hand']
])
# re-center data for the target
covs_train_target = target_org['covs']
MC_target = mean_riemann(covs_train_target)
labs_train_target = target_org['labels']
class_mean_1_target = mean_riemann(
covs_train_target[labs_train_target == 'left_hand'])
class_mean_1_target = np.dot(
invsqrtm(MC_target),
np.dot(class_mean_1_target, invsqrtm(MC_target)))
class_mean_2_target = mean_riemann(
covs_train_target[labs_train_target == 'right_hand'])
class_mean_2_target = np.dot(
invsqrtm(MC_target),
np.dot(class_mean_2_target, invsqrtm(MC_target)))
covs_train_target = np.stack(
[class_mean_1_target, class_mean_2_target])
labs_train_target = np.array(['left_hand', 'right_hand'])
covs_train = np.concatenate([covs_train_source, covs_train_target])
labs_train = np.concatenate([labs_train_source, labs_train_target])
covs_test = target_org_test['covs']
labs_test = target_org_test['labels']
# do the classification
clf = MDM()
clf.fit(covs_train, labs_train)
score_rzt = score_rzt + clf.score(covs_test, labs_test)
score = score_rzt / nrzt
return score
off_events,
event_id,
tmin=2,
tmax=5,
baseline=None)
# Get Epochs data (signal)
off_epochs_data = off_epochs.get_data()
epochs_data = copy.deepcopy(off_epochs_data)
# Get Labels
labels = off_epochs.events[:, -1]
labels_base = copy.deepcopy(labels)
# Covariance Matrix transorm
off_cov_matrix = Covariances(estimator='lwf').transform(off_epochs_data)
# MDM model init and fit
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(off_cov_matrix, labels)
# End of offline training
# EEG stream on the lab network
print("looking for an EEG stream...")
streams = resolve_stream('name', 'openvibeSignal')
# Create a new inlet to read from the stream
inlet = StreamInlet(streams[0])
kmeans = Kmeans(n_clusters=4)
time_window = timeWindowInit(inlet)
time_window_base = copy.deepcopy(time_window)
timeBase = time.time()
ext_signal = _bandpass_filter(raw, frequencies, frequency_range)
###############################################################################
raw_ext = createNewRaw(ext_signal, raw)
###############################################################################
event_id = {'13 Hz': 2, '17 Hz': 4, '21 Hz': 3, 'resting-state': 1}
epochs = Epochs(raw_ext, events, event_id, tmin=2, tmax=5, baseline=None)
t1 = time.time()
cov_ext_trials = Covariances(estimator='lwf').transform(epochs.get_data())
###############################################################################
# Get labels
labels = epochs.events[:, -1]
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(cov_ext_trials, labels)
t2 = time.time()
prediction_labeled = mdm.predict(cov_ext_trials)
print("predict time = " + str(t2 - t1))
print(labels)
print(prediction_labeled)
clf.fit(epochs_data[train_idx], y_train)
pr[test_idx] = clf.predict(epochs_data[test_idx])
print classification_report(labels, pr)
print confusion_matrix(labels, pr)
# spatial patterns
xd = XdawnCovariances(n_components)
Cov = xd.fit_transform(epochs_data, labels)
evoked.data = xd.Xd._patterns.T
evoked.times = np.arange(evoked.data.shape[0])
evoked.plot_topomap(
times=[0, n_components, 2 * n_components, 3 * n_components],
ch_type='grad',
colorbar=False,
size=1.5)
# prototyped covariance matrices
mdm = MDM()
mdm.fit(Cov, labels)
fig, axe = plt.subplots(1, 4)
axe[0].matshow(mdm.covmeans[0])
axe[0].set_title('Class 1 covariance matrix')
axe[1].matshow(mdm.covmeans[1])
axe[1].set_title('Class 2 covariance matrix')
axe[2].matshow(mdm.covmeans[2])
axe[2].set_title('Class 3 covariance matrix')
axe[3].matshow(mdm.covmeans[3])
axe[3].set_title('Class 4 covariance matrix')
plt.show()
clf = Pipeline([('CSP', csp), ('LDA', lda)])
scores = cross_val_score(clf, epochs_data_train, labels, cv=cv, n_jobs=1)
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("CSP + LDA Classification accuracy: %f / Chance level: %f" %
(np.mean(scores), class_balance))
###############################################################################
# Display MDM centroid
mdm = MDM()
mdm.fit(cov_data_train, labels)
fig, axes = plt.subplots(1, 2, figsize=[8, 4])
ch_names = [ch.replace('.', '') for ch in epochs.ch_names]
df = pd.DataFrame(data=mdm.covmeans[0], index=ch_names, columns=ch_names)
g = sns.heatmap(df, ax=axes[0], square=True, cbar=False, xticklabels=2,
yticklabels=2)
g.set_title('Mean covariance - hands')
df = pd.DataFrame(data=mdm.covmeans[1], index=ch_names, columns=ch_names)
g = sns.heatmap(df, ax=axes[1], square=True, cbar=False, xticklabels=2,
yticklabels=2)
plt.xticks(rotation='vertical')
plt.yticks(rotation='horizontal')
g.set_title('Mean covariance - feets')
def get_score(subject=7, runs=[6, 10, 14], event_id=dict(hands=2, feet=3)):
if subject in EXCLUDE_SUBJECTS:
return
tmin, tmax = -1., 4.
weights = np.arange(0.1, 1.0, 0.1)
for weight in weights:
first_sub = 2 if subject == 1 else 1
raw = get_raw(subject, runs)
scores = []
for i in range(first_sub, TRANS_SUBJECT_COUNT):
print(i)
if i == subject or (i in EXCLUDE_SUBJECTS):
continue
raw.append(get_raw(i, runs))
events = find_events(raw, shortest_event=0, stim_channel='STI 014')
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True,
baseline=None, preload=True, verbose=False)
labels = epochs.events[:, -1]
epochs_data_train = 1e6*epochs.get_data()[:, :-1]
cov_data_train = Covariances().transform(epochs_data_train)
target_sample_weight_base = np.ones(EPOCH_COUNT)*weight
others_sample_weight_base = np.ones(
len(epochs)-EPOCH_COUNT)*(1.-weight)
sample_weight = np.hstack(
(target_sample_weight_base, others_sample_weight_base))
others_size = others_sample_weight_base.size
others_index = np.arange(others_size)
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
cv = KFold(n_splits=5, shuffle=True, random_state=42)
train_scores = []
test_scores = []
dumy_array = np.ones(EPOCH_COUNT)
for train_index, test_index in cv.split(dumy_array):
train_index = np.hstack(
(others_index, train_index+others_size))
x = cov_data_train[train_index]
y = labels[train_index]
mdm.fit(x, y, sample_weight=sample_weight[train_index])
score = (mdm.predict(x) == y).sum()/len(train_index)
train_scores.append(score)
test_index = test_index + others_size
y = mdm.predict(cov_data_train[test_index])
score = (y == labels[test_index]).sum()/len(test_index)
test_scores.append(score)
train_score = np.mean(train_scores)
test_score = np.mean(test_scores)
scores.append([subject, i, train_score, test_score])
df = pd.DataFrame(
scores, columns=["subject", "transfer_count", "train_score", "test_score"])
df.to_excel("data/riemann/gradually/test_subject_%d_weight_%e.xlsx" %
(subject, weight), index=False)
for train_idx, test_idx in cv:
y_train, y_test = labels[train_idx], labels[test_idx]
clf.fit(epochs_data[train_idx], y_train)
scores.append(clf.score(epochs_data[test_idx], y_test))
# Printing the results
class_balance = np.mean(labels == labels[0])
class_balance = max(class_balance, 1. - class_balance)
print("Classification accuracy: %f / Chance level: %f" % (np.mean(scores),
class_balance))
# spatial patterns
xd = XdawnCovariances(n_components)
Cov = xd.fit_transform(epochs_data,labels)
evoked.data = xd.Xd._patterns.T
evoked.times = np.arange(evoked.data.shape[0])
evoked.plot_topomap(times=[0, 1, n_components, n_components+1], ch_type='grad',
colorbar=False, size=1.5)
# prototyped covariance matrices
mdm = MDM()
mdm.fit(Cov,labels)
fig,axe = plt.subplots(1,2)
axe[0].matshow(mdm.covmeans[0])
axe[0].set_title('Class 1 covariance matrix')
axe[1].matshow(mdm.covmeans[1])
axe[1].set_title('Class 2 covariance matrix')
plt.show()
frequency_range)
offline_raw = createRaw(filtered_offline_signal, offline_raw, filtered=True)
offline_epochs = Epochs(offline_raw,
offline_events,
event_id,
tmin=0,
tmax=5,
baseline=None)
offline_epochs_data = offline_epochs.get_data()
# Creating ML model
offline_cov_matrix = Covariances(
estimator='lwf').transform(offline_epochs_data)
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(offline_cov_matrix, labels)
# Evoking trials to simulate online input
iter_evoked = epochs.iter_evoked()
epochs_data = offline_epochs_data
time_array = []
pre_predict = mdm.predict(offline_cov_matrix)
print("Labels: ")
print(labels)
for i, evoked in enumerate(iter_evoked):
evoked_raw = createRaw(evoked.data, raw, filtered=False)
## Start Time Counting
class ERPDistance(BaseEstimator, TransformerMixin):
"""ERP distance cov estimator.
This transformer estimates Riemannian distance for ERP covariance matrices.
After estimation of special form ERP covariance matrices using the ERP
transformer, a MDM [1] algorithm is used to compute Riemannian distance.
References:
[1] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass
Brain-Computer Interface Classification by Riemannian Geometry," in IEEE
Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012
"""
def __init__(self, window=500, nfilters=3, subsample=1, metric='riemann',
n_jobs=1):
"""Init."""
self.window = window
self.nfilters = nfilters
self.subsample = subsample
self.metric = metric
self.n_jobs = n_jobs
self._fitted = False
def fit(self, X, y):
"""fit."""
# Create ERP and get cov mat
self.ERP = ERP(self.window, self.nfilters, self.subsample)
train_cov = self.ERP.fit_transform(X, y)
labels_train = self.ERP.labels_train
# Add rest epochs
rest_cov = self._get_rest_cov(X, y)
train_cov = np.concatenate((train_cov, rest_cov), axis=0)
labels_train = np.concatenate((labels_train, [0] * len(rest_cov)))
# fit MDM
self.MDM = MDM(metric=self.metric, n_jobs=self.n_jobs)
self.MDM.fit(train_cov, labels_train)
self._fitted = True
return self
def transform(self, X, y=None):
"""Transform."""
test_cov = self.ERP.transform(X)
dist = self.MDM.transform(test_cov)
dist = dist[:, 1:] - np.atleast_2d(dist[:, 0]).T
return dist
def update_subsample(self, old_sub, new_sub):
"""update subsampling."""
if self._fitted:
self.ERP.update_subsample(old_sub, new_sub)
def _get_rest_cov(self, X, y):
"""Sample rest epochs from data and compute the cov mat."""
ix = np.where(np.diff(y[:, 0]) == 1)[0]
rest = []
offset = - self.window
for i in ix:
start = i + offset - self.window
stop = i + offset
rest.append(self.ERP.erp_cov(X[slice(start, stop)].T))
return np.array(rest)
i = 1
for train_index, test_index in kf.split(X_train):
logging.info(f'Doing fold {i}')
clf_knn = KNearestNeighbor(n_neighbors, metric, n_jobs)
clf_mdm = MDM(metric, n_jobs)
X_train_fold, X_test_fold = X_train[train_index], X_train[test_index]
y_train_fold, y_test_fold = y_train[train_index], y_train[test_index]
clf_knn.fit(X_train_fold, y_train_fold)
y_predicted = clf_knn.predict(X_test_fold)
accuracy = (y_test_fold == y_predicted).sum() / len(y_test_fold)
clf_knn_k_fold.append(clf_knn)
accuracy_list_training_knn.append(accuracy)
clf_mdm.fit(X_train_fold, y_train_fold)
y_predicted = clf_mdm.predict(X_test_fold)
accuracy = (y_test_fold == y_predicted).sum() / len(y_test_fold)
clf_mdm_k_fold.append(clf_mdm)
accuracy_list_training_mdm.append(accuracy)
i += 1
# Testing on test dataset
logging.info('Doing testing')
accuracy_list_testing_knn = []
accuracy_list_testing_mdm = []
X_test, y_test = shuffle(X_test, y_test, random_state=args.seed)
for clf_knn in clf_knn_k_fold:
y_predicted = clf_knn.predict(X_test)
filtered_off_signal = _bandpass_filter(raw_off, frequencies, frequency_range)
raw_off = createOfflineRaw(filtered_off_signal, raw_off)
epochs_off = Epochs(raw_off,
events_off,
event_id,
tmin=0,
tmax=5,
baseline=None)
epochs_data = epochs_off.get_data()
covariance_off = Covariances(estimator='lwf').transform(epochs_data)
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(covariance_off, labels)
# Epochs
iter_evoked = epochs.iter_evoked()
for evoked_number, evoked in enumerate(iter_evoked):
evoked_raw = createOnlineRaw(evoked.data, raw)
time_1 = time.time()
evoked_filtered_signal = _bandpass_filter(evoked_raw, frequencies,
frequency_range)
evoked_filtered_signal = np.array(evoked_filtered_signal)
evoked_filtered_signal = np.expand_dims(evoked_filtered_signal, axis=0)
kf = KFold(n_splits=n_splits)
for train_index, test_index in tqdm(kf.split(covs), total=n_splits):
# split into training and testing datasets
covs_train = covs[train_index]
labs_train = labels[train_index]
covs_test = covs[test_index]
labs_test = labels[test_index]
# reduce the dimensions with ['covpca', 'gpcaRiemann']
for meth in ['covpca', 'gpcaRiemann']:
trf = DR.RDR(n_components=pred, method=meth)
trf.fit(covs_train)
covs_train_red = trf.transform(covs_train)
covs_test_red = trf.transform(covs_test)
clf.fit(covs_train_red, labs_train)
scores[meth].append(clf.score(covs_test_red, labs_test))
# reduce the dimensions with [SELg, SELb]
for meth, sel in zip(['SELg', 'SELb'], [SELg, SELb]):
covs_train_red = covs_train[:, sel, :][:, :, sel]
covs_test_red = covs_test[:, sel, :][:, :, sel]
clf.fit(covs_train_red, labs_train)
scores[meth].append(clf.score(covs_test_red, labs_test))
print('subject ', subject)
# print the scores
for meth in scores.keys():
print(meth, np.mean(scores[meth]))
print('')
CSP_svm_record = []
for fold in range(1, 6):
train = cov_data_bad[index[bad_subject_index] != fold]
train_CSP = epochs_data_train_bad[index[bad_subject_index] != fold]
train_label = labels_bad[index[bad_subject_index] != fold]
test = cov_data_bad[index[bad_subject_index] == fold]
test_CSP = epochs_data_train_bad[index[bad_subject_index] == fold]
test_label = labels_bad[index[bad_subject_index] == fold]
box_length = np.sum([index[bad_subject_index] == fold])
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))
mdm.fit(train, train_label)
pred = mdm.predict(test)
print('MDM: {:4f}'.format(np.sum(pred == test_label) / box_length))
MDM_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
Fgmdm = FgMDM(metric=dict(mean='riemann', distance='riemann'))
Fgmdm.fit(train, train_label)
pred = Fgmdm.predict(test)
print('FGMDM: {:4f}'.format(
np.sum(pred == test_label) / box_length))
FGMDM_record.append(np.sum(pred == test_label) / box_length)
print('-----------------------------------------')
