text: import numpy as np
  import pandas as pd
  from scipy.cluster.hierarchy import dendrogram, linkage
  from scipy.spatial.distance import squareform
  from sklearn.covariance import OAS
  import matplotlib.pyplot as plt
  from mlfinlab.portfolio_optimization.hrp import HierarchicalRiskParity
  
  
  class HierarchicalRiskParityModified(HierarchicalRiskParity):
      
      def allocate(self, asset_prices, covariance, resample_by='B', use_shrinkage=False):
          if not isinstance(asset_prices, pd.DataFrame):
              raise ValueError("Asset prices matrix must be a dataframe")
          if not isinstance(asset_prices.index, pd.DatetimeIndex):
              raise ValueError("Asset prices dataframe must be indexed by date.")
  
          # Calculate the returns
          asset_returns = self._calculate_returns(asset_prices, resample_by=resample_by)
  
          num_assets = asset_returns.shape[1]
          assets = asset_returns.columns
  
          
          ### Using the modified covariance matrix ###
          cov = pd.DataFrame(covariance, columns=assets, index=assets)
          
          
          if use_shrinkage:
              cov = self._shrink_covariance(covariance=cov)
          corr = self._cov2corr(covariance=cov)
  
          # Step-1: Tree Clustering
          distances, self.clusters = self._tree_clustering(correlation=corr)
  
          # Step-2: Quasi Diagnalization
          self.ordered_indices = self._quasi_diagnalization(num_assets, 2 * num_assets - 2)
          self.seriated_distances, self.seriated_correlations = self._get_seriated_matrix(assets=assets,
                                                                                          distances=distances,
                                                                                          correlations=corr)
  
          # Step-3: Recursive Bisection
          self._recursive_bisection(covariances=cov, assets=assets)