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_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
