def test_scores(self):
labels_by_vote1 = clusterer_ensemble_scores(self.original_labels,
self.n_estimators,
self.n_clusters)
assert_equal(labels_by_vote1.shape[0], self.X.shape[0])
# return aligned_labels as well
labels_by_vote2, aligned_labels = clusterer_ensemble_scores(
self.original_labels, self.n_estimators, self.n_clusters,
return_results=True)
assert_equal(labels_by_vote2.shape[0], self.X.shape[0])
assert_equal(aligned_labels.shape, self.original_labels.shape)
# select a different reference base estimator (default is 0)
labels_by_vote3 = clusterer_ensemble_scores(self.original_labels,
self.n_estimators,
self.n_clusters,
reference_idx=1)
assert_equal(labels_by_vote3.shape[0], self.X.shape[0])
# Initialize a set of estimators
estimators = [
KMeans(n_clusters=n_clusters),
MiniBatchKMeans(n_clusters=n_clusters),
AgglomerativeClustering(n_clusters=n_clusters)
]
clf = ClustererEnsemble(estimators, n_clusters=n_clusters)
clf.fit(X)
predicted_labels = clf.labels_
aligned_labels = clf.aligned_labels_
# Clusterer Ensemble without ininializing a new Class
original_labels = np.zeros([X.shape[0], n_estimators])
for i, estimator in enumerate(estimators):
estimator.fit(X)
original_labels[:, i] = estimator.labels_
# Invoke method directly without initialiing a new Class
labels_by_vote1 = clusterer_ensemble_scores(original_labels, n_estimators,
n_clusters)
labels_by_vote2, aligned_labels = clusterer_ensemble_scores(
original_labels, n_estimators, n_clusters, return_results=True)
labels_by_vote3 = clusterer_ensemble_scores(original_labels,
n_estimators,
n_clusters,
reference_idx=1)
