Coverage for src\cognitivefactory\interactive_clustering\clustering\factory.py: 100.00%

1# -*- coding: utf-8 -*- 

2 

3""" 

4* Name: cognitivefactory.interactive_clustering.clustering.factory 

5* Description: The factory method used to easily initialize a constrained clustering algorithm. 

6* Author: Erwan SCHILD 

7* Created: 17/03/2021 

8* Licence: CeCILL-C License v1.0 (https://cecill.info/licences.fr.html) 

9""" 

10 

11# ============================================================================== 

12# IMPORT PYTHON DEPENDENCIES 

13# ============================================================================== 

14 

15from cognitivefactory.interactive_clustering.clustering.abstract import AbstractConstrainedClustering 

16from cognitivefactory.interactive_clustering.clustering.affinity_propagation import ( 

17 AffinityPropagationConstrainedClustering, 

18) 

19from cognitivefactory.interactive_clustering.clustering.dbscan import DBScanConstrainedClustering 

20from cognitivefactory.interactive_clustering.clustering.hierarchical import HierarchicalConstrainedClustering 

21from cognitivefactory.interactive_clustering.clustering.kmeans import KMeansConstrainedClustering 

22from cognitivefactory.interactive_clustering.clustering.mpckmeans import MPCKMeansConstrainedClustering 

23from cognitivefactory.interactive_clustering.clustering.spectral import SpectralConstrainedClustering 

24 

25 

26# ============================================================================== 

27# CLUSTERING FACTORY 

28# ============================================================================== 

29def clustering_factory(algorithm: str = "kmeans", **kargs) -> "AbstractConstrainedClustering": 

30 """ 

31 A factory to create a new instance of a constrained clustering model. 

32 

33 Args: 

34 algorithm (str): The identification of model to instantiate. Can be `"affinity_propagation"`, `"dbscan"`, `"hierarchical"`, `"kmeans"`, `"mpckmeans"` or `"spectral"`. Defaults to `"kmeans"`. 

35 **kargs (dict): Other parameters that can be used in the instantiation. 

36 

37 Warns: 

38 FutureWarning: `clustering.affinity_propagation.AffinityPropagationConstrainedClustering`, `clustering.dbscan.DBScanConstrainedClustering` and `clustering.mpckmeans.MPCKMeansConstrainedClustering` are still in development and are not fully tested : it is not ready for production use. 

39 

40 Raises: 

41 ValueError: if `algorithm` is not implemented. 

42 

43 Returns: 

44 AbstractConstraintsClustering: An instance of constrained clustering model. 

45 

46 Example: 

47 ```python 

48 # Import. 

49 from cognitivefactory.interactive_clustering.clustering.factory import clustering_factory 

50 

51 # Create an instance of kmeans. 

52 clustering_model = clustering_factory( 

53 algorithm="kmeans", 

54 model="COP", 

55 random_seed=42, 

56 ) 

57 ``` 

58 """ 

59 

60 # Check that the requested algorithm is implemented. 

61 if algorithm not in { 

62 "affinity_propagation", 

63 "dbscan", 

64 "hierarchical", 

65 "kmeans", 

66 "mpckmeans", 

67 "spectral", 

68 }: 

69 raise ValueError("The `algorithm` '" + str(algorithm) + "' is not implemented.") 

70 

71 # Initialize 

72 cluster_object: AbstractConstrainedClustering 

73 

74 # Case of Affinity Propagation Constrained Clustering. 

75 if algorithm == "affinity_propagation": 

76 cluster_object = AffinityPropagationConstrainedClustering(**kargs) 

77 

78 # Case of DBScan Constrained Clustering. 

79 elif algorithm == "dbscan": 

80 cluster_object = DBScanConstrainedClustering(**kargs) 

81 

82 # Case of Hierachical Constrained Clustering. 

83 elif algorithm == "hierarchical": 

84 cluster_object = HierarchicalConstrainedClustering(**kargs) 

85 

86 # Case of MPC KMmeans Constrained Clustering. 

87 elif algorithm == "mpckmeans": 

88 cluster_object = MPCKMeansConstrainedClustering(**kargs) 

89 

90 # Case of Spectral Constrained Clustering. 

91 elif algorithm == "spectral": 

92 cluster_object = SpectralConstrainedClustering(**kargs) 

93 

94 # Default case of KMeans Constrained Clustering (algorithm=="kmeans":). 

95 else: 

96 cluster_object = KMeansConstrainedClustering(**kargs) 

97 

98 # Return cluster object. 

99 return cluster_object