How to use the mlxtend.classifier.EnsembleClassifier function in mlxtend

def test_EnsembleClassifier_gridsearch_enumerate_names():

    clf1 = LogisticRegression(random_state=1)
    clf2 = RandomForestClassifier(random_state=1)
    eclf = EnsembleClassifier(clfs=[clf1, clf1, clf2], voting='soft')

    params = {'logisticregression-1__C': [1.0, 100.0],
              'logisticregression-2__C': [1.0, 100.0],
              'randomforestclassifier__n_estimators': [5, 20],}

    grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
    gs = grid.fit(iris.data, iris.target)

def test_EnsembleClassifier_gridsearch():

    clf1 = LogisticRegression(random_state=1)
    clf2 = RandomForestClassifier(random_state=1)
    clf3 = GaussianNB()
    eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='soft')

    params = {'logisticregression__C': [1.0, 100.0],
              'randomforestclassifier__n_estimators': [20, 200],}

    grid = GridSearchCV(estimator=eclf, param_grid=params, cv=5)
    grid.fit(iris.data, iris.target)

    mean_scores = []
    for params, mean_score, scores in grid.grid_scores_:
        mean_scores.append(round(mean_score, 2))
    assert(mean_scores == [0.95, 0.96, 0.96, 0.95])

def test_EnsembleClassifier_weights():

    np.random.seed(123)
    clf1 = LogisticRegression()
    clf2 = RandomForestClassifier()
    clf3 = GaussianNB()
    eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='soft', weights=[1,2,10])

    scores = cross_validation.cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
    scores_mean = (round(scores.mean(), 2))
    assert(scores_mean == 0.93)

def test_EnsembleClassifier():

    np.random.seed(123)
    clf1 = LogisticRegression()
    clf2 = RandomForestClassifier()
    clf3 = GaussianNB()
    eclf = EnsembleClassifier(clfs=[clf1, clf2, clf3], voting='hard')

    scores = cross_validation.cross_val_score(eclf, X, y, cv=5, scoring='accuracy')
    scores_mean = (round(scores.mean(), 2))
    assert(scores_mean == 0.94)

else:
        predictor_dump_path = sys.argv[2]
else:
    predictor_dump_path = None

### Configuration ###

# We use NeuroPred's dataset for training/validation of our predictors.
project_paths.dataset_name = 'neuropred'

ensemble_classifiers = [
    LogisticRegressionCV(Cs = 16, n_jobs = -2, class_weight = 'auto'),
    RandomForestClassifier(n_estimators = 250, bootstrap = True, criterion = 'gini', n_jobs = -2, class_weight = 'auto'),
    SVC(kernel = 'rbf', C = 3.798, probability = True, cache_size = 2400, class_weight = 'auto'),
]
classifiers = [EnsembleClassifier(clfs = ensemble_classifiers, voting = 'hard')]

feature_selector = FeatureSelectionPipeline([
    VarianceThreshold(0.03),
    SelectFdr(alpha = 0.1),
])

### Train the classifier and dump the predictor ###

windows_file = None
predictor_dump_file = None

def open_files():
    global windows_file, predictor_dump_file
    windows_file = open(project_paths.get_window_features_file_path(advanced), 'rb')
    predictor_dump_file = util.open_file(predictor_dump_path, 'wb')