def cluster_metrics(self):
labels_true, labels_pred_1, labels_pred_2 = self.prepare_labels()
metric_funcs = [adjusted_rand_score, adjusted_mutual_info_score, normalized_mutual_info_score,
mutual_info_score, homogeneity_score, completeness_score]
# ------ Label-based metrics ------
self.file.write("\n\nPred = L2 Milestones Pred= L1 Labels Metric\n\n")
for metric in metric_funcs:
try:
score_1 = utils.nsf(metric(labels_true, labels_pred_1))
score_2 = utils.nsf(metric(labels_true, labels_pred_2))
except ValueError as e:
print e
print "Pruning error"
sys.exit()
key = repr(metric).split()[1]
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" % (score_1, score_2, key))
# ------ Precision & Recall ------
try:
for ave in ["micro", "macro", "weighted"]:
key = "precision_" + ave
score_1 = utils.nsf(precision_score(labels_true, labels_pred_1, average = ave))
score_2 = utils.nsf(precision_score(labels_true, labels_pred_2, average = ave))
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" % (score_1, score_2, key))
key = "recall_" + ave
score_1 = utils.nsf(recall_score(labels_true, labels_pred_1, average = ave))
score_2 = utils.nsf(recall_score(labels_true, labels_pred_2, average = ave))
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" % (score_1, score_2, key))
except ValueError as e:
print e
print "Pruning error"
sys.exit()
self.file.write("\nSilhouette Scores Global\n")
# ------ Silhouette Scores ------
for layer in sorted(self.silhouette_scores_global):
score = self.silhouette_scores_global[layer]
self.file.write("%3.3f %s\n" % (round(Decimal(score), 2), layer))
self.file.write("\nSilhouette Scores Weighted\n")
# ------ Silhouette Scores ------
for layer in sorted(self.silhouette_scores_weighted):
score = self.silhouette_scores_weighted[layer]
self.file.write("%3.3f %s\n" % (round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores1\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_1):
score = self.dunn_scores_1[layer]
self.file.write("%3.3f %s\n" % (round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores2\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_2):
score = self.dunn_scores_2[layer]
self.file.write("%3.3f %s\n" % (round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores3\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_3):
score = self.dunn_scores_3[layer]
self.file.write("%3.3f %s\n" % (round(Decimal(score), 2), layer))
# ------ Visualizing changepoints on broken barh ------
viz = {}
for cp in self.list_of_cp:
cp_all_data = (self.map_cp2frm[cp], self.map_cp2milestones[cp], self.map_cp2level1[cp],
self.map_cp2surgemes[cp], self.map_cp2surgemetransitions[cp])
utils.dict_insert_list(self.map_cp2demonstrations[cp], cp_all_data, viz)
data = [self.label_based_scores_1, self.label_based_scores_2,
self.silhouette_scores_global, self.dunn_scores_1, self.dunn_scores_2, self.dunn_scores_3,
self.level2_silhoutte_global, self.level2_dunn_1, self.level2_dunn_2, self.level2_dunn_3, viz,
self.silhouette_scores_weighted, self.level2_silhoutte_weighted]
pickle.dump(data, open(self.metrics_picklefile, "wb"))
return data
def cluster_metrics(self):
"""
Computes metrics and returns as single dictionary.
"""
labels_true, labels_pred = self.prepare_labels()
metric_funcs = [adjusted_rand_score, adjusted_mutual_info_score, normalized_mutual_info_score,
mutual_info_score, homogeneity_score, completeness_score]
# ------ Label-based metrics ------
utils.print_and_write("\n\nPred= L1 Labels Metric\n\n", self.log)
for metric in metric_funcs:
score_1 = round(Decimal(metric(labels_true, labels_pred)), 2)
key = repr(metric).split()[1]
self.label_based_scores_1[key] = score_1
utils.print_and_write(("%3.3f %s\n" % (score_1, key)), self.log)
# ------ Precision & Recall ------
utils.print_and_write("\n Precision & Recall scores \n", self.log)
for ave in ["micro", "macro", "weighted"]:
key = "precision_" + ave
score_1 = utils.nsf(precision_score(labels_true, labels_pred, average = ave))
self.label_based_scores_1[key] = score_1
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score_1), 2), key), self.log)
key = "recall_" + ave
score_1 = utils.nsf(recall_score(labels_true, labels_pred, average = ave))
self.label_based_scores_1[key] = score_1
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score_1), 2), key), self.log)
utils.print_and_write("\nDunn Scores1\n", self.log)
# ------ Dunn Scores # 1------
for layer in sorted(self.dunn_scores_1):
score = self.dunn_scores_1[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
utils.print_and_write("\nDunn Scores2\n", self.log)
# ------ Dunn Scores # 2------
for layer in sorted(self.dunn_scores_2):
score = self.dunn_scores_2[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
utils.print_and_write("\nDunn Scores3\n", self.log)
# ------ Dunn Scores #3 ------
for layer in sorted(self.dunn_scores_3):
score = self.dunn_scores_3[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
# ------ Visualizing changepoints on broken barh ------
viz = {}
for cp in self.list_of_cp:
cp_all_data = (self.map_cp2frm[cp], self.map_cp2cluster[cp], self.map_cp2surgemetransitions[cp], self.map_cp2surgemes[cp])
utils.dict_insert_list(self.map_cp2demonstrations[cp], cp_all_data, viz)
data = [self.label_based_scores_1, self.silhouette_score_global, self.dunn_scores_1,
self.dunn_scores_2, self.dunn_scores_3, viz, self.silhouette_score_weighted]
return data
def cluster_metrics(self):
labels_true, labels_pred_1, labels_pred_2 = self.prepare_labels()
metric_funcs = [
adjusted_rand_score, adjusted_mutual_info_score,
normalized_mutual_info_score, mutual_info_score, homogeneity_score,
completeness_score
]
# ------ Label-based metrics ------
self.file.write(
"\n\nPred = L2 Milestones Pred= L1 Labels Metric\n\n")
for metric in metric_funcs:
try:
score_1 = utils.nsf(metric(labels_true, labels_pred_1))
score_2 = utils.nsf(metric(labels_true, labels_pred_2))
except ValueError as e:
print e
print "Pruning error"
sys.exit()
key = repr(metric).split()[1]
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" %
(score_1, score_2, key))
# ------ Precision & Recall ------
try:
for ave in ["micro", "macro", "weighted"]:
key = "precision_" + ave
score_1 = utils.nsf(
precision_score(labels_true, labels_pred_1, average=ave))
score_2 = utils.nsf(
precision_score(labels_true, labels_pred_2, average=ave))
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" %
(score_1, score_2, key))
key = "recall_" + ave
score_1 = utils.nsf(
recall_score(labels_true, labels_pred_1, average=ave))
score_2 = utils.nsf(
recall_score(labels_true, labels_pred_2, average=ave))
self.label_based_scores_1[key] = score_1
self.label_based_scores_2[key] = score_2
self.file.write("%3.3f %3.3f %s\n" %
(score_1, score_2, key))
except ValueError as e:
print e
print "Pruning error"
sys.exit()
self.file.write("\nSilhouette Scores Global\n")
# ------ Silhouette Scores ------
for layer in sorted(self.silhouette_scores_global):
score = self.silhouette_scores_global[layer]
self.file.write("%3.3f %s\n" %
(round(Decimal(score), 2), layer))
self.file.write("\nSilhouette Scores Weighted\n")
# ------ Silhouette Scores ------
for layer in sorted(self.silhouette_scores_weighted):
score = self.silhouette_scores_weighted[layer]
self.file.write("%3.3f %s\n" %
(round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores1\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_1):
score = self.dunn_scores_1[layer]
self.file.write("%3.3f %s\n" %
(round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores2\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_2):
score = self.dunn_scores_2[layer]
self.file.write("%3.3f %s\n" %
(round(Decimal(score), 2), layer))
self.file.write("\nDunn Scores3\n")
# ------ Dunn Scores ------
for layer in sorted(self.dunn_scores_3):
score = self.dunn_scores_3[layer]
self.file.write("%3.3f %s\n" %
(round(Decimal(score), 2), layer))
# ------ Visualizing changepoints on broken barh ------
viz = {}
for cp in self.list_of_cp:
cp_all_data = (self.map_cp2frm[cp], self.map_cp2milestones[cp],
self.map_cp2level1[cp], self.map_cp2surgemes[cp],
self.map_cp2surgemetransitions[cp])
utils.dict_insert_list(self.map_cp2demonstrations[cp], cp_all_data,
viz)
data = [
self.label_based_scores_1, self.label_based_scores_2,
self.silhouette_scores_global, self.dunn_scores_1,
self.dunn_scores_2, self.dunn_scores_3,
self.level2_silhoutte_global, self.level2_dunn_1,
self.level2_dunn_2, self.level2_dunn_3, viz,
self.silhouette_scores_weighted, self.level2_silhoutte_weighted
]
pickle.dump(data, open(self.metrics_picklefile, "wb"))
return data
def cluster_metrics(self):
"""
Computes metrics and returns as single dictionary.
"""
labels_true, labels_pred = self.prepare_labels()
metric_funcs = [adjusted_rand_score, adjusted_mutual_info_score, normalized_mutual_info_score,
mutual_info_score, homogeneity_score, completeness_score]
# ------ Label-based metrics ------
utils.print_and_write("\n\nPred= L1 Labels Metric\n\n", self.log)
for metric in metric_funcs:
score_1 = round(Decimal(metric(labels_true, labels_pred)), 2)
key = repr(metric).split()[1]
self.label_based_scores_1[key] = score_1
utils.print_and_write(("%3.3f %s\n" % (score_1, key)), self.log)
# ------ Precision & Recall ------
utils.print_and_write("\n Precision & Recall scores \n", self.log)
for ave in ["micro", "macro", "weighted"]:
key = "precision_" + ave
score_1 = utils.nsf(precision_score(labels_true, labels_pred, average = ave))
self.label_based_scores_1[key] = score_1
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score_1), 2), key), self.log)
key = "recall_" + ave
score_1 = utils.nsf(recall_score(labels_true, labels_pred, average = ave))
self.label_based_scores_1[key] = score_1
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score_1), 2), key), self.log)
utils.print_and_write("\nDunn Scores1\n", self.log)
# ------ Dunn Scores # 1------
for layer in sorted(self.dunn_scores_1):
score = self.dunn_scores_1[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
utils.print_and_write("\nDunn Scores2\n", self.log)
# ------ Dunn Scores # 2------
for layer in sorted(self.dunn_scores_2):
score = self.dunn_scores_2[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
utils.print_and_write("\nDunn Scores3\n", self.log)
# ------ Dunn Scores #3 ------
for layer in sorted(self.dunn_scores_3):
score = self.dunn_scores_3[layer]
utils.print_and_write("%3.3f %s\n" % (round(Decimal(score), 2), layer), self.log)
# ------ Visualizing changepoints on broken barh ------
viz = {}
for cp in self.list_of_cp:
cp_all_data = (self.map_cp2frm[cp], self.map_cp2cluster[cp], self.map_cp2surgemetransitions[cp], self.map_cp2surgemes[cp])
utils.dict_insert_list(self.map_cp2demonstrations[cp], cp_all_data, viz)
data = [self.label_based_scores_1, self.silhouette_score_global, self.dunn_scores_1,
self.dunn_scores_2, self.dunn_scores_3, viz, self.silhouette_score_weighted]
return data
