def getClusterLabelsFamilies(experiment_id, selected_cluster):
selected_cluster = int(selected_cluster)
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
labels_families = clustering.getClusterLabelsFamilies(
experiment, selected_cluster)
return jsonify(labels_families)
def getNumElements(experiment_id, selected_cluster):
selected_cluster = int(selected_cluster)
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
cluster = clustering.clusters[selected_cluster]
res = {}
res['num_elements'] = cluster.numInstances()
return jsonify(res)
def getClustersLabels(experiment_id):
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
# Do not consider empty clusters for visualization
clusters = []
for c in range(clustering.num_clusters):
# if clustering.clusters[c].numInstances() > 0:
clusters.append({'id': c, 'label': clustering.clusters[c].label})
return jsonify({'clusters': clusters})
def getClusterInstancesVisu(exp_id, selected_cluster, c_e_r, num_results):
num_results = int(num_results)
selected_cluster = int(selected_cluster)
exp = updateCurrentExperiment(exp_id)
clustering = ClusteringExp.from_json(exp.output_dir())
ids = {}
ids[selected_cluster] = clustering.getClusterInstancesVisu(
selected_cluster, num_results, random=True)[c_e_r]
return jsonify(ids)
def getClusterLabelFamilyIds(exp_id, selected_cluster, label, family,
num_results):
selected_cluster = int(selected_cluster)
num_results = int(num_results)
exp = updateCurrentExperiment(exp_id)
clustering = ClusteringExp.from_json(exp.output_dir())
ids = clustering.getClusterLabelFamilyIds(exp, selected_cluster, label,
family)
return jsonify(listResultWebFormat(ids, num_results))
def getClusterStats(experiment_id):
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
num_clusters = clustering.num_clusters
num_instances_v = []
labels = []
for c in range(num_clusters):
instances_in_cluster = clustering.clusters[c].instances_ids
num_instances = len(instances_in_cluster)
# the empty clusters are not displayed
# if num_instances > 0:
num_instances_v.append(num_instances)
#labels.append('c_' + str(c))
labels.append(clustering.clusters[c].label)
barplot = BarPlot(labels)
dataset = PlotDataset(num_instances_v, 'Num. Instances')
barplot.add_dataset(dataset)
return jsonify(barplot.to_json())
def getClusterPredictedLabel(experiment_id, selected_cluster):
selected_cluster = int(selected_cluster)
experiment = updateCurrentExperiment(experiment_id)
clustering = ClusteringExp.from_json(experiment.output_dir())
predicted_label = clustering.getClusterLabel(selected_cluster)
return predicted_label