Single Linkage Clustering Of Edit Distance Matrix With Distance Threshold Stopping Criterion

I'm trying to assign flat, single-linkage clusters to sequence IDs separated by an edit distance < n, given a square distance matrix. I believe scipy.cluster.hierarchy.fclusterd

Solution 1:

You did not set the metric parameter.

The default then is metric='euclidean', not precomputed.

Solution 2:

Figured it out by passing linkage() to fcluster(), which supports metric='precomputed' unlike fclusterdata().

fcluster(linkage(condensed_dm, metric='precomputed'), criterion='distance', t=20)

Answer :

import pandas as pd
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import linkage, fcluster

cols = ['a', 'b', 'c', 'd']

df = pd.DataFrame([{'a': 0, 'b': 29467, 'c': 35, 'd': 13},
                   {'a': 29467, 'b': 0, 'c': 29468, 'd': 29470},
                   {'a': 35, 'b': 29468, 'c': 0, 'd': 38},
                   {'a': 13, 'b': 29470, 'c': 38, 'd': 0}],
                  index=cols)

dm_cnd = squareform(df.values)

clusters_20 = fcluster(linkage(dm_cnd, metric='precomputed'), criterion='distance', t=20)
clusters_50 = fcluster(linkage(dm_cnd, metric='precomputed'), criterion='distance', t=50)
clusters_100 = fcluster(linkage(dm_cnd, metric='precomputed'), criterion='distance', t=100)

names_clusters_20 = {n: c for n, c inzip(cols, clusters_20)}
names_clusters_50 = {n: c for n, c inzip(cols, clusters_50)}
names_clusters_100 = {n: c for n, c inzip(cols, clusters_100)}

names_clusters_20
>>>{'a': 1, 'b': 3, 'c': 2, 'd': 1}

names_clusters_50
>>>{'a': 1, 'b': 2, 'c': 1, 'd': 1}

names_clusters_100
>>>{'a': 1, 'b': 2, 'c': 1, 'd': 1}

As a function:

import pandas as pd
from scipy.spatial.distance import squareform
from scipy.cluster.hierarchy import fcluster, linkage

defcluster_df(df, method='single', threshold=100):
    '''
    Accepts a square distance matrix as an indexed DataFrame and returns a dict of index keyed flat clusters 
    Performs single linkage clustering by default, see scipy.cluster.hierarchy.linkage docs for others
    '''

    dm_cnd = squareform(df.values)
    clusters = fcluster(linkage(dm_cnd,
                                method=method,
                                metric='precomputed'),
                        criterion='distance',
                        t=threshold)
    names_clusters = {s:c for s, c inzip(df.columns, clusters)}
return names_clusters