from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from __future__ import division
from random import shuffle
from random import seed
from sklearn.metrics import adjusted_rand_score
import matplotlib.pyplot as plt
from concept_formation.trestle import TrestleTree
from concept_formation.cluster import cluster
from concept_formation.datasets import load_rb_wb_03
from concept_formation.preprocessor import ObjectVariablizer
seed(0)
towers = load_rb_wb_03()
shuffle(towers)
towers = towers[:60]
variablizer = ObjectVariablizer()
towers = [variablizer.transform(t) for t in towers]
tree = TrestleTree()
clusters = cluster(tree, towers, maxsplit=10)
human_labels = [tower['_human_cluster_label'] for tower in towers]
x = [num_splits for num_splits in range(1,len(clusters)+1)]
y = [adjusted_rand_score(human_labels, split) for split in clusters]
plt.plot(x, y, label="TRESTLE")
plt.title("TRESTLE Clustering Accuracy (Given Human Ground Truth)")
plt.ylabel("Adjusted Rand Index (Agreement Correcting for Chance)")
plt.xlabel("# of Splits of Trestle Tree")
plt.legend(loc=4)
plt.show()