Clustering Estimators

The skfolio.cluster module complements sklearn.cluster with additional clustering estimators including the HierarchicalClustering that forms hierarchical clusters from a distance matrix. It is used in the following portfolio optimizations:

• HierarchicalRiskParity

• HierarchicalEqualRiskContribution

• NestedClustersOptimization

Example:

from skfolio.cluster import HierarchicalClustering
from skfolio.datasets import load_sp500_dataset
from skfolio.distance import PearsonDistance
from skfolio.preprocessing import prices_to_returns

prices = load_sp500_dataset()
X = prices_to_returns(prices)

distance_estimator = PearsonDistance()
distance_estimator.fit(X)
distance = distance_estimator.distance_

model = HierarchicalClustering()
model.fit(distance)
print(model.linkage_matrix_)