Plot dendrogram using sklearn.AgglomerativeClustering

Here is a simple function for taking a hierarchical clustering model from sklearn and plotting it using the scipy dendrogram function. Seems like graphing functions are often not directly supported in sklearn. You can find an interesting discussion of that related to the pull request for this plot_dendrogram code snippet here.

I'd clarify that the use case you describe (defining number of clusters) is available in scipy: after you've performed the hierarchical clustering using scipy's linkage you can cut the hierarchy to whatever number of clusters you want using fcluster with number of clusters specified in the t argument and criterion='maxclust' argument.

Use the scipy implementation of agglomerative clustering instead. Here is an example.

from scipy.cluster.hierarchy import dendrogram, linkagedata = [[0., 0.], [0.1, -0.1], [1., 1.], [1.1, 1.1]]Z = linkage(data)dendrogram(Z)  

You can find documentation for linkage here and documentation for dendrogram here.

I came across the exact same problem some time ago. The way I managed to plot the damn dendogram was using the software package ete3. This package is able to flexibly plot trees with various options. The only difficulty was to convert sklearn's children_ output to the Newick Tree format that can be read and understood by ete3. Furthermore, I need to manually compute the dendrite's span because that information was not provided with the children_. Here is a snippet of the code I used. It computes the Newick tree and then shows the ete3 Tree datastructure. For more details on how to plot, take a look here

import numpy as npfrom sklearn.cluster import AgglomerativeClusteringimport ete3def build_Newick_tree(children,n_leaves,X,leaf_labels,spanner):    """    build_Newick_tree(children,n_leaves,X,leaf_labels,spanner)    Get a string representation (Newick tree) from the sklearn    AgglomerativeClustering.fit output.    Input:        children: AgglomerativeClustering.children_        n_leaves: AgglomerativeClustering.n_leaves_        X: parameters supplied to AgglomerativeClustering.fit        leaf_labels: The label of each parameter array in X        spanner: Callable that computes the dendrite's span    Output:        ntree: A str with the Newick tree representation    """    return go_down_tree(children,n_leaves,X,leaf_labels,len(children)+n_leaves-1,spanner)[0]+';'def go_down_tree(children,n_leaves,X,leaf_labels,nodename,spanner):    """    go_down_tree(children,n_leaves,X,leaf_labels,nodename,spanner)    Iterative function that traverses the subtree that descends from    nodename and returns the Newick representation of the subtree.    Input:        children: AgglomerativeClustering.children_        n_leaves: AgglomerativeClustering.n_leaves_        X: parameters supplied to AgglomerativeClustering.fit        leaf_labels: The label of each parameter array in X        nodename: An int that is the intermediate node name whos            children are located in children[nodename-n_leaves].        spanner: Callable that computes the dendrite's span    Output:        ntree: A str with the Newick tree representation    """    nodeindex = nodename-n_leaves    if nodename<n_leaves:        return leaf_labels[nodeindex],np.array([X[nodeindex]])    else:        node_children = children[nodeindex]        branch0,branch0samples = go_down_tree(children,n_leaves,X,leaf_labels,node_children[0])        branch1,branch1samples = go_down_tree(children,n_leaves,X,leaf_labels,node_children[1])        node = np.vstack((branch0samples,branch1samples))        branch0span = spanner(branch0samples)        branch1span = spanner(branch1samples)        nodespan = spanner(node)        branch0distance = nodespan-branch0span        branch1distance = nodespan-branch1span        nodename = '({branch0}:{branch0distance},{branch1}:{branch1distance})'.format(branch0=branch0,branch0distance=branch0distance,branch1=branch1,branch1distance=branch1distance)        return nodename,nodedef get_cluster_spanner(aggClusterer):    """    spanner = get_cluster_spanner(aggClusterer)    Input:        aggClusterer: sklearn.cluster.AgglomerativeClustering instance    Get a callable that computes a given cluster's span. To compute    a cluster's span, call spanner(cluster)    The cluster must be a 2D numpy array, where the axis=0 holds    separate cluster members and the axis=1 holds the different    variables.    """    if aggClusterer.linkage=='ward':        if aggClusterer.affinity=='euclidean':            spanner = lambda x:np.sum((x-aggClusterer.pooling_func(x,axis=0))**2)    elif aggClusterer.linkage=='complete':        if aggClusterer.affinity=='euclidean':            spanner = lambda x:np.max(np.sum((x[:,None,:]-x[None,:,:])**2,axis=2))        elif aggClusterer.affinity=='l1' or aggClusterer.affinity=='manhattan':            spanner = lambda x:np.max(np.sum(np.abs(x[:,None,:]-x[None,:,:]),axis=2))        elif aggClusterer.affinity=='l2':            spanner = lambda x:np.max(np.sqrt(np.sum((x[:,None,:]-x[None,:,:])**2,axis=2)))        elif aggClusterer.affinity=='cosine':            spanner = lambda x:np.max(np.sum((x[:,None,:]*x[None,:,:]))/(np.sqrt(np.sum(x[:,None,:]*x[:,None,:],axis=2,keepdims=True))*np.sqrt(np.sum(x[None,:,:]*x[None,:,:],axis=2,keepdims=True))))        else:            raise AttributeError('Unknown affinity attribute value {0}.'.format(aggClusterer.affinity))    elif aggClusterer.linkage=='average':        if aggClusterer.affinity=='euclidean':            spanner = lambda x:np.mean(np.sum((x[:,None,:]-x[None,:,:])**2,axis=2))        elif aggClusterer.affinity=='l1' or aggClusterer.affinity=='manhattan':            spanner = lambda x:np.mean(np.sum(np.abs(x[:,None,:]-x[None,:,:]),axis=2))        elif aggClusterer.affinity=='l2':            spanner = lambda x:np.mean(np.sqrt(np.sum((x[:,None,:]-x[None,:,:])**2,axis=2)))        elif aggClusterer.affinity=='cosine':            spanner = lambda x:np.mean(np.sum((x[:,None,:]*x[None,:,:]))/(np.sqrt(np.sum(x[:,None,:]*x[:,None,:],axis=2,keepdims=True))*np.sqrt(np.sum(x[None,:,:]*x[None,:,:],axis=2,keepdims=True))))        else:            raise AttributeError('Unknown affinity attribute value {0}.'.format(aggClusterer.affinity))    else:        raise AttributeError('Unknown linkage attribute value {0}.'.format(aggClusterer.linkage))    return spannerclusterer = AgglomerativeClustering(n_clusters=2,compute_full_tree=True) # You can set compute_full_tree to 'auto', but I left it this way to get the entire tree plottedclusterer.fit(X) # X for whatever you want to fitspanner = get_cluster_spanner(clusterer)newick_tree = build_Newick_tree(clusterer.children_,clusterer.n_leaves_,X,leaf_labels,spanner) # leaf_labels is a list of labels for each entry in Xtree = ete3.Tree(newick_tree)tree.show()