Extracting clusters from seaborn clustermap

Question

I am using the seaborn clustermap to create clusters and visually it works great (this example produces very similar results).

However I am having trouble figuring out how to programmatically extract the clusters. For instance, in the example link, how could I find out that 1-1 rh, 1-1 lh, 5-1 rh, 5-1 lh make a good cluster? Visually it's easy. I am trying to use methods of looking through the data, and dendrograms but I'm having little success

EDIT Code from example:

import pandas as pd import seaborn as sns sns.set(font="monospace")  df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0) used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17] used_columns = (df.columns.get_level_values("network")                           .astype(int)                           .isin(used_networks)) df = df.loc[:, used_columns]  network_pal = sns.cubehelix_palette(len(used_networks),                                     light=.9, dark=.1, reverse=True,                                     start=1, rot=-2) network_lut = dict(zip(map(str, used_networks), network_pal))  networks = df.columns.get_level_values("network") network_colors = pd.Series(networks).map(network_lut)  cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)  result = sns.clustermap(df.corr(), row_colors=network_colors, method="average",                col_colors=network_colors, figsize=(13, 13), cmap=cmap) 

How can I pull what models are in which clusters out of result?

EDIT2 The result does carry with it a linkage in with the dendrogram_col which I THINK would work with fcluster. But the threshold value to select that is confusing me. I would assume that values in the heatmap that are higher than the threshold would get clustered together?

Answer 1

While using result.linkage.dendrogram_col or result.linkage.dendrogram_row will currently work, it seems to be an implementation detail. The safest route is to first compute the linkages explicitly and pass them to the clustermap function, which has row_linkage and col_linkage parameters just for that.

Replacing the last line in your example (result = ...) with the following code gives the same result as before, but you will also have row_linkage and col_linkage variables that you can use with fcluster etc.

from scipy.spatial import distance from scipy.cluster import hierarchy  correlations = df.corr() correlations_array = np.asarray(df.corr())  row_linkage = hierarchy.linkage(     distance.pdist(correlations_array), method='average')  col_linkage = hierarchy.linkage(     distance.pdist(correlations_array.T), method='average')  sns.clustermap(correlations, row_linkage=row_linkage, col_linkage=col_linkage, row_colors=network_colors, method="average",                col_colors=network_colors, figsize=(13, 13), cmap=cmap) 

In this particular example, the code could be simplified more since the correlations array is symmetric and therefore row_linkage and col_linkage will be identical.

Note: A previous answer included a call to distance.squareshape according to what the code in seaborn does, but that is a bug.