def __train_metis_model(self, c):
matrices = FileManager().get_ensembling_matrices(
self.name, self.are_clusters_fixed)
for key, value in matrices.items():
if key == 'CO':
model = MetisClustering(value, c)
assignations = model.clusterize()[1]
FileManager().save_results(assignations, self.name, key,
self.are_clusters_fixed, 'Metis')
else:
features = len(value[0])
graph = nx.Graph()
graph.add_nodes_from(list(range(self.num_rows)))
graph.add_nodes_from(
list(range(self.num_rows, self.num_rows + features)))
for cnt_row, rows in enumerate(value):
for cnt_col, col in enumerate(rows):
graph.add_edge(cnt_row,
self.num_rows + cnt_col,
weight=col)
model = MetisClustering(value, c, graph)
assignations = model.clusterize()[1][:self.num_rows]
FileManager().save_results(assignations, self.name, key,
self.are_clusters_fixed, 'Metis')
Validator(self.name, self.numerical, self.ground_truth, 'Metis',
self.are_clusters_fixed).validate_consensus()
def __train_spectral_model(self, c):
matrices = FileManager().get_ensembling_matrices(
self.name, self.are_clusters_fixed)
for key, value in matrices.items():
model = Spectral(value, c)
assignations = model.clusterize()
FileManager().save_results(assignations, self.name, key,
self.are_clusters_fixed, 'Spectral')
Validator(self.name, self.numerical, self.ground_truth, 'Spectral',
self.are_clusters_fixed).validate_consensus()
def __train_ahc_model(self, c, link=None, algorithm='SL'):
matrices = FileManager().get_ensembling_matrices(
self.name, self.are_clusters_fixed)
for key, value in matrices.items():
model = Agglomerative(value, c, ahc_linkage=link)
assignations = model.clusterize()
FileManager().save_results(assignations, self.name, key,
self.are_clusters_fixed, algorithm)
Validator(self.name, self.numerical, self.ground_truth, algorithm,
self.are_clusters_fixed).validate_consensus()