def analyze_clusters(self, **kwargs):
"""
This function analyzes the clusters according to the provided method
:param kwargs: a dictionary of cluster-analysis-specific parameters
:type kwargs: `dict`
:return: assigned_clean_by_class, an array of arrays that contains what data points where classified as clean.
:rtype: `np.ndarray`
"""
self.set_params(**kwargs)
if not self.clusters_by_class:
self.cluster_activations()
analyzer = ClusteringAnalyzer()
if self.cluster_analysis == 'smaller':
self.assigned_clean_by_class, self.poisonous_clusters \
= analyzer.analyze_by_size(self.clusters_by_class)
elif self.cluster_analysis == 'relative-size':
self.assigned_clean_by_class, self.poisonous_clusters \
= analyzer.analyze_by_relative_size(self.clusters_by_class)
elif self.cluster_analysis == 'distance':
self.assigned_clean_by_class, self.poisonous_clusters \
= analyzer.analyze_by_distance(self.clusters_by_class,
separated_activations=self.red_activations_by_class)
elif self.cluster_analysis == 'silhouette-scores':
self.assigned_clean_by_class, self.poisonous_clusters \
= analyzer.analyze_by_sihouette_score(self.clusters_by_class,
reduced_activations_by_class=self.red_activations_by_class)
else:
raise ValueError(
"Unsupported cluster analysis technique " + self.cluster_analysis)
return self.assigned_clean_by_class
def analyze_clusters(self, **kwargs):
"""
This function analyzes the clusters according to the provided method
:param kwargs: a dictionary of cluster-analysis-specific parameters
:type kwargs: `dict`
:return: (report, assigned_clean_by_class), where the report is a json object and assigned_clean_by_class
is an array of arrays that contains what data points where classified as clean.
:rtype: `tuple(json, np.ndarray)`
"""
self.set_params(**kwargs)
if not self.clusters_by_class:
self.cluster_activations()
analyzer = ClusteringAnalyzer()
if self.cluster_analysis == 'smaller':
self.assigned_clean_by_class, self.poisonous_clusters, report \
= analyzer.analyze_by_size(self.clusters_by_class)
elif self.cluster_analysis == 'relative-size':
self.assigned_clean_by_class, self.poisonous_clusters, report \
= analyzer.analyze_by_relative_size(self.clusters_by_class)
elif self.cluster_analysis == 'distance':
self.assigned_clean_by_class, self.poisonous_clusters, report \
= analyzer.analyze_by_distance(self.clusters_by_class,
separated_activations=self.red_activations_by_class)
elif self.cluster_analysis == 'silhouette-scores':
self.assigned_clean_by_class, self.poisonous_clusters, report \
= analyzer.analyze_by_silhouette_score(self.clusters_by_class,
reduced_activations_by_class=self.red_activations_by_class)
else:
raise ValueError(
"Unsupported cluster analysis technique " + self.cluster_analysis)
# Add to the report current parameters used to run the defence and the analysis summary
report = dict(list(report.items()) + list(self.get_params().items()))
import json
jreport = json.dumps(report)
return jreport, self.assigned_clean_by_class