def run(self, clustering_parameters, matrix_handler, data_handler, workspaceHandler):
############################
# Clustering exploration
############################
self.notify("Exploration Started", [])
self.timer.start("Clustering Exploration")
clusterings = ClusteringExplorer( clustering_parameters,
matrix_handler,
workspaceHandler,
scheduling_tools.build_scheduler(clustering_parameters["global"]["control"],
self.observer),
AlgorithmRunParametersGenerator(clustering_parameters,
matrix_handler),
self.observer).run()
self.notify("Clusterings Created", {"number_of_clusters":len(clusterings)})
self.timer.stop("Clustering Exploration")
######################
# First filtering
######################
self.timer.start("Clustering Filtering")
selected_clusterings, not_selected_clusterings = ClusteringFilter(clustering_parameters["clustering"]["evaluation"],
matrix_handler).filter(clusterings)
self.notify("Filter", {"selected":len(selected_clusterings.keys()),"not_selected":len(not_selected_clusterings.keys())})
self.timer.stop("Clustering Filtering")
if selected_clusterings == {}:
return None
######################
# Clustering scoring
######################
self.timer.start("Evaluation")
analyzer = AnalysisRunner(scheduling_tools.build_scheduler(
clustering_parameters["global"]["control"],
self.observer),
selected_clusterings,
AnalysisPopulator(matrix_handler,
data_handler,
clustering_parameters))
analyzer.evaluate()
self.timer.stop("Evaluation")
######################
# Choose the best clustering
######################
self.timer.start("Selection")
best_clustering_id, all_scores = BestClusteringSelector(clustering_parameters).choose_best(selected_clusterings)
self.timer.stop("Selection")
return best_clustering_id, selected_clusterings, not_selected_clusterings, all_scores
def run (self, clustering):
"""
Refine a clustering recursively using a k-means over each cluster.
New clusters obtained from a cluster must have no noise and
"""
max_partitions = self.refinement_parameters["max_partitions"]
try_step = int(max(1, float(max_partitions) / self.refinement_parameters["tries_per_cluster"]))
matrix = self.matrixHandler.distance_matrix
new_clusters = []
for cluster in clustering.clusters:
base_id = cluster.id
# The initial clustering is added to the list of new clusters.
# With this 'trick' the initial cluster also enters the competition for the best clustering price.
clusterings = {base_id:{"type":"refined_base",
"clustering": Clustering([cluster]),
"parameters": {}}}
submatrix = get_submatrix(matrix, cluster.all_elements)
# Proceed with some K Medoids partitions
# TODO: Generate parameters with parameter generator
for k in range(2,max_partitions,try_step):
clustering = self.repartition_with_kmedoids(cluster, k, submatrix)
clusterings["%s_%d"%(base_id,k)] = {"type":"refined",
"clustering": clustering,
"parameters": {"k":k}}
# Evaluate all clusterings and pick the best one
AnalysisRunner(scheduling_tools.build_scheduler(
self.clustering_parameters["clustering"]["control"],
self.observer),
clusterings,
AnalysisPopulator(self.matrixHandler,
self.trajectoryHandler,
self.clustering_parameters)).evaluate()
best_clustering_id, all_scores = BestClusteringSelector(self.clustering_parameters).choose_best(clusterings) # @UnusedVariable
new_clusters.extend(clusterings[best_clustering_id]["clustering"].clusters)
# Convert all new clusters in the new clustering
return {"type":"refined_clustering",
"clustering": Clustering(new_clusters),
"parameters": self.refinement_parameters}
def run(self, clustering_parameters, matrixHandler, workspaceHandler,
trajectoryHandler):
############################
# Clustering exploration
############################
self.notify("Exploration Started", [])
self.timer.start("Clustering Exploration")
clusterings = ClusteringExplorer(
clustering_parameters, matrixHandler, workspaceHandler,
scheduling_tools.build_scheduler(
clustering_parameters["global"]["control"], self.observer),
AlgorithmRunParametersGenerator(clustering_parameters,
matrixHandler),
self.observer).run()
self.notify("Clusterings Created",
{"number_of_clusters": len(clusterings)})
self.timer.stop("Clustering Exploration")
######################
# First filtering
######################
self.timer.start("Clustering Filtering")
selected_clusterings, not_selected_clusterings = ClusteringFilter(
clustering_parameters["clustering"]["evaluation"],
matrixHandler).filter(clusterings)
self.notify(
"Filter", {
"selected": len(selected_clusterings.keys()),
"not_selected": len(not_selected_clusterings.keys())
})
self.timer.stop("Clustering Filtering")
if selected_clusterings == {}:
return None
######################
# Clustering scoring
######################
self.timer.start("Evaluation")
analyzer = AnalysisRunner(
scheduling_tools.build_scheduler(
clustering_parameters["global"]["control"], self.observer),
selected_clusterings,
AnalysisPopulator(matrixHandler, trajectoryHandler,
clustering_parameters))
analyzer.evaluate()
self.timer.stop("Evaluation")
######################
# Choose the best clustering
######################
self.timer.start("Selection")
best_clustering_id, all_scores = BestClusteringSelector(
clustering_parameters).choose_best(selected_clusterings)
self.timer.stop("Selection")
return best_clustering_id, selected_clusterings, not_selected_clusterings, all_scores
def run(self, clustering):
"""
Refine a clustering recursively using a k-means over each cluster.
New clusters obtained from a cluster must have no noise and
"""
max_partitions = self.refinement_parameters["max_partitions"]
try_step = int(
max(
1,
float(max_partitions) /
self.refinement_parameters["tries_per_cluster"]))
matrix = self.matrixHandler.distance_matrix
new_clusters = []
for cluster in clustering.clusters:
base_id = cluster.id
# The initial clustering is added to the list of new clusters.
# With this 'trick' the initial cluster also enters the competition for the best clustering price.
clusterings = {
base_id: {
"type": "refined_base",
"clustering": Clustering([cluster]),
"parameters": {}
}
}
submatrix = get_submatrix(matrix, cluster.all_elements)
# Proceed with some K Medoids partitions
# TODO: Generate parameters with parameter generator
for k in range(2, max_partitions, try_step):
clustering = self.repartition_with_kmedoids(
cluster, k, submatrix)
clusterings["%s_%d" % (base_id, k)] = {
"type": "refined",
"clustering": clustering,
"parameters": {
"k": k
}
}
# Evaluate all clusterings and pick the best one
AnalysisRunner(
scheduling_tools.build_scheduler(
self.clustering_parameters["clustering"]["control"],
self.observer), clusterings,
AnalysisPopulator(self.matrixHandler, self.trajectoryHandler,
self.clustering_parameters)).evaluate()
best_clustering_id, all_scores = BestClusteringSelector(
self.clustering_parameters).choose_best(
clusterings) # @UnusedVariable
new_clusters.extend(
clusterings[best_clustering_id]["clustering"].clusters)
# Convert all new clusters in the new clustering
return {
"type": "refined_clustering",
"clustering": Clustering(new_clusters),
"parameters": self.refinement_parameters
}
