kmedoids.process() - Code Metrics - Inspection of "Merge branch '0.8.dev'." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

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by Andrei

created 2018-04-15 18:36 UTC

kmedoids.process() B

↳ Parent: kmedoids

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"""!

@brief Cluster analysis algorithm: K-Medoids (PAM - Partitioning Around Medoids).
@details Based on book description:
         - A.K. Jain, R.C Dubes, Algorithms for Clustering Data. 1988.
         - L. Kaufman, P.J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis. 1990.

@authors Andrei Novikov ([email protected])
@date 2014-2018
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""


from pyclustering.cluster.encoder import type_encoding;

from pyclustering.utils import median;
from pyclustering.utils.metric import distance_metric, type_metric;

from pyclustering.core.wrapper import ccore_library;

import pyclustering.core.kmedoids_wrapper as wrapper;

from pyclustering.core.metric_wrapper import metric_wrapper;


class kmedoids:
    """!
    @brief Class represents clustering algorithm K-Medoids (another one title is PAM - Partitioning Around Medoids).
    @details The algorithm is less sensitive to outliers tham K-Means. The principle difference between K-Medoids and K-Medians is that
             K-Medoids uses existed points from input data space as medoids, but median in K-Medians can be unreal object (not from
             input data space).
             
             CCORE option can be used to use core pyclustering - C/C++ shared library for processing that significantly increases performance.
    
    Clustering example:
    @code
        # load list of points for cluster analysis
        sample = read_sample(path);
        
        # set random initial medoids
        initial_medoids = [1, 10];
        
        # create instance of K-Medoids algorithm
        kmedoids_instance = kmedoids(sample, initial_medoids);
        
        # run cluster analysis and obtain results
        kmedoids_instance.process();
        clusters = kmedoids_instance.get_clusters();
        
        # show allocated clusters
        print(clusters);
    @endcode

    Metric foc calculation distance between points can be specified by parameter additional 'metric':
    @code
        from pyclustering.utils.metric import type_metric, distance_metric;

        # create Minkowski distance metric with degree equals to '2'
        metric = distance_metric(type_metric.MINKOWSKI, degree=2);

        # create K-Medoids algorithm with specific distance metric
        kmedoids_instance = kmedoids(sample, initial_medoids, metric=metric);

        # run cluster analysis and obtain results
        kmedoids_instance.process();
        clusters = kmedoids_instance.get_clusters();
    @endcode
    
    """
    
    
    def __init__(self, data, initial_index_medoids, tolerance = 0.25, ccore = True, **kwargs):
        """!
        @brief Constructor of clustering algorithm K-Medoids.
        
        @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
        @param[in] initial_index_medoids (list): Indexes of intial medoids (indexes of points in input data).
        @param[in] tolerance (double): Stop condition: if maximum value of distance change of medoids of clusters is less than tolerance than algorithm will stop processing.
        @param[in] ccore (bool): If specified than CCORE library (C++ pyclustering library) is used for clustering instead of Python code.
        @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'metric').

        Keyword Args:
            metric (distance_metric): Metric that is used for distance calculation between two points.

        """
        self.__pointer_data = data;
        self.__clusters = [];
        self.__medoids = [ data[medoid_index] for medoid_index in initial_index_medoids ];
        self.__medoid_indexes = initial_index_medoids;
        self.__tolerance = tolerance;
        self.__metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE));

        if self.__metric is None:
            self.__metric = distance_metric(type_metric.EUCLIDEAN_SQUARE);

        self.__ccore = ccore and self.__metric.get_type() != type_metric.USER_DEFINED;

        if self.__ccore:
            self.__ccore = ccore_library.workable();


    def process(self):
        """!
        @brief Performs cluster analysis in line with rules of K-Medoids algorithm.
        
        @remark Results of clustering can be obtained using corresponding get methods.
        
        @see get_clusters()
        @see get_medoids()
        
        """
        
        if (self.__ccore is True):
            ccore_metric = metric_wrapper.create_instance(self.__metric);

            self.__clusters = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, ccore_metric.get_pointer());
            self.__medoids, self.__medoid_indexes = self.__update_medoids();
        
        else:
            changes = float('inf');
             
            stop_condition = self.__tolerance * self.__tolerance;
             
            while changes > stop_condition:
                self.__clusters = self.__update_clusters();
                updated_medoids, update_medoid_indexes = self.__update_medoids();
             
                changes = max([distance_metric(type_metric.EUCLIDEAN_SQUARE)(self.__medoids[index], updated_medoids[index]) for index in range(len(updated_medoids))]);    # Fast solution
                 
                self.__medoids = updated_medoids;
                self.__medoid_indexes = update_medoid_indexes;


    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
        
        @see process()
        @see get_medoids()
        
        """
        
        return self.__clusters;
    
    
    def get_medoids(self):
        """!
        @brief Returns list of medoids of allocated clusters.
        
        @see process()
        @see get_clusters()
        
        """

        return self.__medoids;


    def get_cluster_encoding(self):
        """!
        @brief Returns clustering result representation type that indicate how clusters are encoded.
        
        @return (type_encoding) Clustering result representation.
        
        @see get_clusters()
        
        """
        
        return type_encoding.CLUSTER_INDEX_LIST_SEPARATION;


    def __update_clusters(self):
        """!
        @brief Calculate distance to each point from the each cluster. 
        @details Nearest points are captured by according clusters and as a result clusters are updated.
        
        @return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data.
        
        """
        
        clusters = [[self.__medoid_indexes[i]] for i in range(len(self.__medoids))];
        for index_point in range(len(self.__pointer_data)):
            if index_point in self.__medoid_indexes:
                continue;

            index_optim = -1;
            dist_optim = float('Inf');
            
            for index in range(len(self.__medoids)):
                dist = self.__metric(self.__pointer_data[index_point], self.__medoids[index]);
                
                if (dist < dist_optim) or (index is 0):
                    index_optim = index;
                    dist_optim = dist;
            
            clusters[index_optim].append(index_point);
        
        return clusters;
    
    
    def __update_medoids(self):
        """!
        @brief Find medoids of clusters in line with contained objects.
        
        @return (list) list of medoids for current number of clusters.
        
        """
         
        medoids = [[] for _ in range(len(self.__clusters))];
        medoid_indexes = [-1] * len(self.__clusters);
        
        for index in range(len(self.__clusters)):
            medoid_index = median(self.__pointer_data, self.__clusters[index], metric=self.__metric);
            medoids[index] = self.__pointer_data[medoid_index];
            medoid_indexes[index] = medoid_index;
             
        return medoids, medoid_indexes;

1			"""!
2
3			@brief Cluster analysis algorithm: K-Medoids (PAM - Partitioning Around Medoids).
4			@details Based on book description:
5			- A.K. Jain, R.C Dubes, Algorithms for Clustering Data. 1988.
6			- L. Kaufman, P.J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis. 1990.
7
8			@authors Andrei Novikov ([email protected])
9			@date 2014-2018
10			@copyright GNU Public License
11
12			@cond GNU_PUBLIC_LICENSE
13			PyClustering is free software: you can redistribute it and/or modify
14			it under the terms of the GNU General Public License as published by
15			the Free Software Foundation, either version 3 of the License, or
16			(at your option) any later version.
17
18			PyClustering is distributed in the hope that it will be useful,
19			but WITHOUT ANY WARRANTY; without even the implied warranty of
20			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
21			GNU General Public License for more details.
22
23			You should have received a copy of the GNU General Public License
24			along with this program. If not, see <http://www.gnu.org/licenses/>.
25			@endcond
26
27			"""
28
29
30			from pyclustering.cluster.encoder import type_encoding;
31
32			from pyclustering.utils import median;
33			from pyclustering.utils.metric import distance_metric, type_metric;
34
35			from pyclustering.core.wrapper import ccore_library;
36
37			import pyclustering.core.kmedoids_wrapper as wrapper;
38
39			from pyclustering.core.metric_wrapper import metric_wrapper;
40
41
42			class kmedoids:
43			"""!
44			@brief Class represents clustering algorithm K-Medoids (another one title is PAM - Partitioning Around Medoids).
45			@details The algorithm is less sensitive to outliers tham K-Means. The principle difference between K-Medoids and K-Medians is that
46			K-Medoids uses existed points from input data space as medoids, but median in K-Medians can be unreal object (not from
47			input data space).
48
49			CCORE option can be used to use core pyclustering - C/C++ shared library for processing that significantly increases performance.
50
51			Clustering example:
52			@code
53			# load list of points for cluster analysis
54			sample = read_sample(path);
55
56			# set random initial medoids
57			initial_medoids = [1, 10];
58
59			# create instance of K-Medoids algorithm
60			kmedoids_instance = kmedoids(sample, initial_medoids);
61
62			# run cluster analysis and obtain results
63			kmedoids_instance.process();
64			clusters = kmedoids_instance.get_clusters();
65
66			# show allocated clusters
67			print(clusters);
68			@endcode
69
70			Metric foc calculation distance between points can be specified by parameter additional 'metric':
71			@code
72			from pyclustering.utils.metric import type_metric, distance_metric;
73
74			# create Minkowski distance metric with degree equals to '2'
75			metric = distance_metric(type_metric.MINKOWSKI, degree=2);
76
77			# create K-Medoids algorithm with specific distance metric
78			kmedoids_instance = kmedoids(sample, initial_medoids, metric=metric);
79
80			# run cluster analysis and obtain results
81			kmedoids_instance.process();
82			clusters = kmedoids_instance.get_clusters();
83			@endcode
84
85			"""
86
87
88			def __init__(self, data, initial_index_medoids, tolerance = 0.25, ccore = True, **kwargs):
89			"""!
90			@brief Constructor of clustering algorithm K-Medoids.
91
92			@param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
93			@param[in] initial_index_medoids (list): Indexes of intial medoids (indexes of points in input data).
94			@param[in] tolerance (double): Stop condition: if maximum value of distance change of medoids of clusters is less than tolerance than algorithm will stop processing.
95			@param[in] ccore (bool): If specified than CCORE library (C++ pyclustering library) is used for clustering instead of Python code.
96			@param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'metric').
97
98			Keyword Args:
99			metric (distance_metric): Metric that is used for distance calculation between two points.
100
101			"""
102			self.__pointer_data = data;
103			self.__clusters = [];
104			self.__medoids = [ data[medoid_index] for medoid_index in initial_index_medoids ];
105			self.__medoid_indexes = initial_index_medoids;
106			self.__tolerance = tolerance;
107			self.__metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE));
108
109			if self.__metric is None:
110			self.__metric = distance_metric(type_metric.EUCLIDEAN_SQUARE);
111
112			self.__ccore = ccore and self.__metric.get_type() != type_metric.USER_DEFINED;
113
114			if self.__ccore:
115			self.__ccore = ccore_library.workable();
116
117
118			def process(self):
119			"""!
120			@brief Performs cluster analysis in line with rules of K-Medoids algorithm.
121
122			@remark Results of clustering can be obtained using corresponding get methods.
123
124			@see get_clusters()
125			@see get_medoids()
126
127			"""
128
129			if (self.__ccore is True):
130			ccore_metric = metric_wrapper.create_instance(self.__metric);
131
132			self.__clusters = wrapper.kmedoids(self.__pointer_data, self.__medoid_indexes, self.__tolerance, ccore_metric.get_pointer());
133			self.__medoids, self.__medoid_indexes = self.__update_medoids();
134
135			else:
136			changes = float('inf');
137
138			stop_condition = self.__tolerance * self.__tolerance;
139
140			while changes > stop_condition:
141			self.__clusters = self.__update_clusters();
142			updated_medoids, update_medoid_indexes = self.__update_medoids();
143
144			changes = max([distance_metric(type_metric.EUCLIDEAN_SQUARE)(self.__medoids[index], updated_medoids[index]) for index in range(len(updated_medoids))]); # Fast solution
145
146			self.__medoids = updated_medoids;
147			self.__medoid_indexes = update_medoid_indexes;
148
149
150			def get_clusters(self):
151			"""!
152			@brief Returns list of allocated clusters, each cluster contains indexes of objects in list of data.
153
154			@see process()
155			@see get_medoids()
156
157			"""
158
159			return self.__clusters;
160
161
162			def get_medoids(self):
163			"""!
164			@brief Returns list of medoids of allocated clusters.
165
166			@see process()
167			@see get_clusters()
168
169			"""
170
171			return self.__medoids;
172
173
174			def get_cluster_encoding(self):
175			"""!
176			@brief Returns clustering result representation type that indicate how clusters are encoded.
177
178			@return (type_encoding) Clustering result representation.
179
180			@see get_clusters()
181
182			"""
183
184			return type_encoding.CLUSTER_INDEX_LIST_SEPARATION;
185
186
187			def __update_clusters(self):
188			"""!
189			@brief Calculate distance to each point from the each cluster.
190			@details Nearest points are captured by according clusters and as a result clusters are updated.
191
192			@return (list) updated clusters as list of clusters where each cluster contains indexes of objects from data.
193
194			"""
195
196			clusters = [[self.__medoid_indexes[i]] for i in range(len(self.__medoids))];
197			for index_point in range(len(self.__pointer_data)):
198			if index_point in self.__medoid_indexes:
199			continue;
200
201			index_optim = -1;
202			dist_optim = float('Inf');
203
204			for index in range(len(self.__medoids)):
205			dist = self.__metric(self.__pointer_data[index_point], self.__medoids[index]);
206
207			if (dist < dist_optim) or (index is 0):
208			index_optim = index;
209			dist_optim = dist;
210
211			clusters[index_optim].append(index_point);
212
213			return clusters;
214
215
216			def __update_medoids(self):
217			"""!
218			@brief Find medoids of clusters in line with contained objects.
219
220			@return (list) list of medoids for current number of clusters.
221
222			"""
223
224			medoids = [[] for _ in range(len(self.__clusters))];
225			medoid_indexes = [-1] * len(self.__clusters);
226
227			for index in range(len(self.__clusters)):
228			medoid_index = median(self.__pointer_data, self.__clusters[index], metric=self.__metric);
229			medoids[index] = self.__pointer_data[medoid_index];
230			medoid_indexes[index] = medoid_index;
231
232			return medoids, medoid_indexes;

annoviko / pyclustering

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