import sys

import pandas as pd

import numpy as np

import networkx as nx

import matplotlib.pyplot as plt

from sklearn.metrics import adjusted_rand_score

sys.path.insert(1, '../../utils/')

from utils import fetch_ucr_dataset_online

from kgraph import kGraph

if __name__ == '__main__':

X, y = fetch_ucr_dataset_online("Coffee")

# Executing kGraph

clf = kGraph(n_clusters=len(set(y)),n_lengths=10,n_jobs=4)

clf.fit(X)

print("Selected length: ",clf.optimal_length)

print("ARI score: ",adjusted_rand_score(clf.labels_,y))

# Plotgraphoid

clf.show_graphoids(group=True,save_fig=True,namefile='Trace_kgraph')

# Plot representative patterns for each cluster

nb_patterns = 1

nodes = clf.interprete(nb_patterns=nb_patterns)

plt.figure(figsize=(10,4*nb_patterns))

count = 0

for j in range(nb_patterns):

for i,node in enumerate(nodes.keys()):

count += 1

plt.subplot(nb_patterns,len(nodes.keys()),count)

mean,sup,inf = clf.get_node_ts(X=X,node=nodes[node][j][0])

plt.fill_between(x=list(range(int(clf.optimal_length))),y1=inf,y2=sup,alpha=0.2)

plt.plot(mean,color='black')

plt.plot(inf,color='black',alpha=0.6,linestyle='--')

plt.plot(sup,color='black',alpha=0.6,linestyle='--')

plt.title('node {} for cluster {}: \n (representativity: {:.3f} \n exclusivity : {:.3f})'.format(nodes[node][j][0],node,nodes[node][j][3],nodes[node][j][2]))

plt.tight_layout()

plt.savefig('Trace_cluster_interpretation.jpg')

plt.close()