def save_cluster_metrics(self, points, predictions, key, level2_mode = False): try: silhoutte_global = metrics.silhouette_score(points, predictions, metric='euclidean') silhoutte_weighted = utils.silhoutte_weighted(points, predictions) self.silhouette_scores_global[key] = silhoutte_global self.silhouette_scores_weighted[key] = silhoutte_weighted if level2_mode: self.level2_silhoutte_global.append(silhoutte_global) self.level2_silhoutte_weighted.append(silhoutte_weighted) except ValueError as e: pass # dunn_scores = cluster_evaluation.dunn_index(points, predictions, means) dunn_scores = [0, 0, 0] if (dunn_scores[0] is not None) and (dunn_scores[1] is not None) and (dunn_scores[2] is not None): self.dunn_scores_1[key] = dunn_scores[0] self.dunn_scores_2[key] = dunn_scores[1] self.dunn_scores_3[key] = dunn_scores[2] if level2_mode: self.level2_dunn_1.append(dunn_scores[0]) self.level2_dunn_2.append(dunn_scores[1]) self.level2_dunn_3.append(dunn_scores[2])
def save_cluster_metrics(self, points, predictions, key): """ Utility function calculates clustering metrics whenever clustering is performed in the algorithm. """ if key == 'level1': self.silhouette_score_global = metrics.silhouette_score(points, predictions, metric='euclidean') self.silhouette_score_weighted = utils.silhoutte_weighted(points, predictions) # dunn_scores = cluster_evaluation.dunn_index(points, predictions, means) dunn_scores = [0,0,0] if (dunn_scores[0] is not None) and (dunn_scores[1] is not None) and (dunn_scores[2] is not None): self.dunn_scores_1[key] = dunn_scores[0] self.dunn_scores_2[key] = dunn_scores[1] self.dunn_scores_3[key] = dunn_scores[2]
def save_cluster_metrics(self, points, predictions, key): """ Utility function calculates clustering metrics whenever clustering is performed in the algorithm. """ if key == 'level1': self.silhouette_score_global = metrics.silhouette_score(points, predictions, metric='euclidean') self.silhouette_score_weighted = utils.silhoutte_weighted(points, predictions) # dunn_scores = cluster_evaluation.dunn_index(points, predictions, means) dunn_scores = [0,0,0] if (dunn_scores[0] is not None) and (dunn_scores[1] is not None) and (dunn_scores[2] is not None): self.dunn_scores_1[key] = dunn_scores[0] self.dunn_scores_2[key] = dunn_scores[1] self.dunn_scores_3[key] = dunn_scores[2]
def save_cluster_metrics(self,
points,
predictions,
key,
level2_mode=False):
try:
silhoutte_global = metrics.silhouette_score(points,
predictions,
metric='euclidean')
silhoutte_weighted = utils.silhoutte_weighted(points, predictions)
self.silhouette_scores_global[key] = silhoutte_global
self.silhouette_scores_weighted[key] = silhoutte_weighted
if level2_mode:
self.level2_silhoutte_global.append(silhoutte_global)
self.level2_silhoutte_weighted.append(silhoutte_weighted)
except ValueError as e:
pass
# dunn_scores = cluster_evaluation.dunn_index(points, predictions, means)
dunn_scores = [0, 0, 0]
if (dunn_scores[0] is not None) and (dunn_scores[1]
is not None) and (dunn_scores[2]
is not None):
self.dunn_scores_1[key] = dunn_scores[0]
self.dunn_scores_2[key] = dunn_scores[1]
self.dunn_scores_3[key] = dunn_scores[2]
if level2_mode:
self.level2_dunn_1.append(dunn_scores[0])
self.level2_dunn_2.append(dunn_scores[1])
self.level2_dunn_3.append(dunn_scores[2])