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- import scipy.cluster.hierarchy as sch
- import numpy as np
- def cluster_corr(corr_array, threshold=None, inplace=False):
- """
- Rearranges the correlation matrix, corr_array, so that groups of highly
- correlated variables are next to eachother
-
- Parameters
- ----------
- corr_array : pandas.DataFrame or numpy.ndarray
- a NxN correlation matrix
-
- Returns
- -------
- corr_array : a NxN correlation matrix with the columns and rows rearranged
- linkage : linkage of distances
- labels : cluster labels
- idx : sorted incides for original labels
- """
- pairwise_distances = sch.distance.pdist(corr_array)
- linkage = sch.linkage(pairwise_distances, method='complete')
- cluster_distance_threshold = pairwise_distances.max()/2 if threshold is None else threshold
- labels = sch.fcluster(linkage, cluster_distance_threshold, criterion='distance')
- idx = np.argsort(labels)
-
- if not inplace:
- corr_array = corr_array.copy()
-
- return corr_array[idx, :][:, idx], linkage, labels, idx
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