<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 sklearn Agglomerative
I can't get this to work properly. The values returned by fit_predict below should essentially be
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
 [6, 6, 6, 6, 6, 6, 6, 6, 6, 6,  6,  6,  6,  6,  6,  6]]
i.e. clusters are samples 0
Step1:  Scipy Hclust
<ASSISTANT_TASK:>
Python Code:
cl = AgglomerativeClustering(16, compute_full_tree=True, affinity='precomputed', linkage='complete')
np.unique(cl.fit_predict(wip_0.data), return_counts=True)
Explanation: sklearn Agglomerative
I can't get this to work properly. The values returned by fit_predict below should essentially be
[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
 [6, 6, 6, 6, 6, 6, 6, 6, 6, 6,  6,  6,  6,  6,  6,  6]]
i.e. clusters are samples 0:15, with 6 runs each
End of explanation
def does_cluster(distmat, ranges):
    hcl = hierarchy.complete(distmat.condensed_form())
    ids = distmat.ids
    tree = skbio.TreeNode.from_linkage_matrix(hcl, ids)
    tree = ete3.Tree.from_skbio(tree)
    mono = 0
    counts = Counter()
    for start, stop in ranges:
        m, phy, _ = tree.check_monophyly(ids[start:stop], target_attr="name", unrooted=True)
        mono += 1 if m else 0
        #print(start, '-', stop, "---", phy)
        counts[phy] += 1
    return mono, counts
def count_clustering(groups):
    wipc = Counter()
    ipc = Counter()
    for wip, ip in pairs:
        _, c = does_cluster(wip, groups)
        wipc.update(c)
        _, c = does_cluster(ip, groups)
        ipc.update(c)
    print(sum(wipc.values()), "groups")
    return wipc, ipc
print(count_clustering(indjap))
print(count_clustering(samples))
print(sum(wipc.values()))
print(sum(ipc.values()))
Explanation: Scipy Hclust
End of explanation
<END_TASK>