def setUp(self):
self.X, self.y = load_breast_cancer(return_X_y=True)
n_clusters = 5
n_estimators = 3
# Initialize a set of estimators
estimators = [KMeans(n_clusters=n_clusters),
MiniBatchKMeans(n_clusters=n_clusters),
AgglomerativeClustering(n_clusters=n_clusters)]
self.estimator = ClustererEnsemble(estimators, n_clusters=n_clusters)
self.estimator.fit(self.X)
if __name__ == "__main__":
# Define data file and read X and y
random_state = 42
X, y = load_breast_cancer(return_X_y=True)
n_clusters = 5
n_estimators = 3
# 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(