How to use the pyriemann.classification.TSclassifier function in pyriemann

def test_TSclassifier():
    """Test TS Classifier"""
    covset = generate_cov(40, 3)
    labels = np.array([0, 1]).repeat(20)

    assert_raises(TypeError, TSclassifier, clf='666')
    clf = TSclassifier()
    clf.fit(covset, labels)
    clf.predict(covset)
    clf.predict_proba(covset)

def test_TSclassifier():
    """Test TS Classifier"""
    covset = generate_cov(40, 3)
    labels = np.array([0, 1]).repeat(20)

    assert_raises(TypeError, TSclassifier, clf='666')
    clf = TSclassifier()
    clf.fit(covset, labels)
    clf.predict(covset)
    clf.predict_proba(covset)

import seaborn as sns
import matplotlib.pyplot as plt

from collections import OrderedDict
from moabb.datasets.bnci import BNCI2014001

from moabb.datasets.alex_mi import AlexMI
from moabb.datasets.physionet_mi import PhysionetMI

datasets = [AlexMI(with_rest=True),
            BNCI2014001(),
            PhysionetMI(with_rest=True, feets=False)]

pipelines = OrderedDict()
pipelines['MDM'] = make_pipeline(Covariances('oas'), MDM())
pipelines['TS'] = make_pipeline(Covariances('oas'), TSclassifier())
pipelines['CSP+LDA'] = make_pipeline(Covariances('oas'), CSP(8), LDA())

context = MotorImageryMultiClasses(datasets=datasets, pipelines=pipelines)

results = context.evaluate(verbose=True)

for p in results.keys():
    results[p].to_csv('../../results/MotorImagery/MultiClass/%s.csv' % p)

results = pd.concat(results.values())
print(results.groupby('Pipeline').mean())

res = results.pivot(values='Score', columns='Pipeline')
sns.lmplot(data=res, x='CSP+LDA', y='TS', fit_reg=False)
plt.xlim(0.25, 1)
plt.ylim(0.25, 1)

###############################################################################
# Classification with Minimum distance to mean
mdm = MDM(metric=dict(mean='riemann', distance='riemann'))

# Use scikit-learn Pipeline with cross_val_score function
scores = cross_val_score(mdm, 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)
print("MDM Classification accuracy: %f / Chance level: %f" % (np.mean(scores),
                                                              class_balance))

###############################################################################
# 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)
print("Tangent space Classification accuracy: %f / Chance level: %f" %
      (np.mean(scores), class_balance))
###############################################################################
# Classification with CSP + logistic regression

# Assemble a classifier
lr = LogisticRegression()
csp = CSP(n_components=4, reg='ledoit_wolf', log=True)

clf = Pipeline([('CSP', csp), ('LogisticRegression', lr)])

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, './')