Fix ConsensusCluster.fit_predict to use consensus matrix

[sharifhsn]

Fixes #25.

What does this fix?

ConsensusCluster.fit_predict() was ignoring the consensus matrix and running a single AgglomerativeClustering on the raw data:

def fit_predict(self, data):
    if self.z_score:
        data = self._z_score(data)
    return self.cluster(n_clusters=self.n_clusters, linkage=self.linkage).fit_predict(data)

This bypasses fit() entirely — the H=100 resamplings that build the consensus matrix never run. The method now calls fit() to build the consensus matrix, then clusters on 1 - Mk with metric='precomputed':

def fit_predict(self, data):
    self.fit(data)
    distance_matrix = 1 - self.Mk
    return AgglomerativeClustering(
        n_clusters=self.n_clusters, metric="precomputed", linkage=self.linkage
    ).fit_predict(distance_matrix)

This matches the consensus clustering paper and the R FlowSOM implementation.

Notes

Performance: fit_predict now does real work (H=100 resamplings on 100 SOM codes), taking ~425ms instead of ~0.4ms. This is the intended behavior — the old version was silently skipping the consensus step.

self.cluster parameter: The final clustering on the consensus distance matrix hardcodes AgglomerativeClustering with metric="precomputed" rather than using the pluggable self.cluster class. This is intentional — metric="precomputed" requires an algorithm that supports it, and a user-supplied clustering class may not. The user's cluster class is still used for the H resamplings inside fit().

linkage="ward" incompatibility: AgglomerativeClustering with metric="precomputed" does not support linkage="ward". The default is "average" which works fine. A user who explicitly passes linkage="ward" would get a ValueError from sklearn — this is an existing limitation of clustering on a precomputed distance matrix, not something introduced by this fix.

One file, +6/-4 lines. All 38 tests pass.

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