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from .clustering import ReportingClustering
from .cluster import SOMCluster
import attr
@attr.s
class ClusteringFactory(object):
# algorithms = attr.ib(init=True)
@classmethod
def inferred(cls, x):
pass
def from_som(self, dataset, som, nb_clusters, **kwargs): # algorithm, nb_clusters=8, ngrams=1, random_state=None, vars=None):
id2members = dict.fromkeys(range(nb_clusters), set()) # cluster id => members set mapping
som.cluster(nb_clusters, random_state=kwargs.get('random_state', None))
# som.cluster(algorithm=self.algorithms[algorithm](nb_clusters, kwargs.get('random_state', None)))
for i, arr in enumerate(som.bmus): # iterate through the array of shape [nb_datapoints, 2]. Each row is the coordinates
# of the neuron the datapoint gets attributed to (closest distance)
attributed_cluster = som.clusters[arr[0], arr[1]] # >= 0
id2members[attributed_cluster].add(dataset.datapoints[i])
def ex1(a_cluster):
return [_ for _ in a_cluster]
def ex2(datapoints, attribute):
return datapoints[str(attribute)]
return ReportingClustering([SOMCluster(cluster_members) for cluster_members in id2members.values()],
str(dataset)+'-'+str(som),
ex1,
ex2,
)
boromir674 /
so-magic
