Parse pmml files and convert it to sklearn kmeans models.
>>> from pmml2sklearn import pmml2sklearn
>>> parsed_model = pmml2sklearn("your_pmml_file.pmml")
>>> # Cluster centers with cluster names parsed from pmml file:
>>> print(parsed_model.clusters) name center
0 cluster_0 43.641748692028024
1 cluster_1 29.32112701093236
2 cluster_2 59.892706731733405
3 cluster_3 9.410157007171932
4 cluster_4 117.94557522123894
>>> # Sklearn kmeans model generated with cluster centers:
>>> print(parsed_model.kmeans)KMeans(algorithm='auto', copy_x=True,
init=array([[ 43.64175],
[ 29.32113],
[ 59.89271],
[ 9.41016],
[117.94558]]),
max_iter=300, n_clusters=5, n_init=1, n_jobs=None,
precompute_distances='auto', random_state=1, tol=0.0001, verbose=0)