def plotDBSCAN(DBSValues, DBSClasses):
X = DBSValues
y = DBSClasses.values.flatten()
#Play with the parameters here
#default settings: eps=0.5, min_samples=5
DBSModel = DBSCAN(eps=0.2, min_samples=5).fit(X)
DBSexpected = y
DBSpredicted = DBSModel.labels_
#print(DBSexpected)
#print(DBSpredicted)
f_performance_measures = open("Clustering/DBS_performance_measures.txt", 'w')
f_performance_measures.write("-----------------Model----------------")
f_performance_measures.write("\n")
f_performance_measures.write(DBSModel.__str__())
f_performance_measures.write("\n")
f_performance_measures.write("--------------------------------------")
f_performance_measures.write("\n")
f_performance_measures.write("Homogeneity: %0.3f" % metrics.homogeneity_score(DBSexpected, DBSpredicted))
f_performance_measures.write("\n")
f_performance_measures.write("Completeness: %0.3f" % metrics.completeness_score(DBSexpected, DBSpredicted))
f_performance_measures.write("\n")
f_performance_measures.write("V-measure: %0.3f" % metrics.v_measure_score(DBSexpected, DBSpredicted))
f_performance_measures.write("\n")
f_performance_measures.write("Adjusted Rand Index: %0.3f" % metrics.adjusted_rand_score(DBSexpected, DBSpredicted))
f_performance_measures.write("\n")
f_performance_measures.write("Adjusted Mutual Information: %0.3f" % metrics.adjusted_mutual_info_score(DBSexpected, DBSpredicted))
f_performance_measures.write("\n")
f_performance_measures.write("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, DBSpredicted))
f_performance_measures.close()
core_samples_mask = np.zeros_like(DBSModel.labels_, dtype=bool)
core_samples_mask[DBSModel.core_sample_indices_] = True
unique_labels = set(y)
colors = [plt.cm.Spectral(each)
for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = [0, 0, 0, 1]
class_member_mask = (y == k)
xy = X[class_member_mask & core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
markeredgecolor='k', markersize=14)
xy = X[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
markeredgecolor='k', markersize=6)
plt.savefig("Clustering/cluster_DBScan.png")
