"""! @brief Integration-tests for K-Medoids algorithm. @authors Andrei Novikov (pyclustering@yandex.ru) @date 2014-2020 @copyright BSD-3-Clause """ import unittest # Generate images without having a window appear. import matplotlib matplotlib.use('Agg') from pyclustering.cluster.tests.kmedoids_templates import kmedoids_test_template from pyclustering.cluster.kmedoids import kmedoids from pyclustering.samples.definitions import SIMPLE_SAMPLES, SIMPLE_ANSWERS from pyclustering.utils import read_sample from pyclustering.utils.metric import type_metric, distance_metric from pyclustering.core.tests import remove_library class KmedoidsIntegrationTest(unittest.TestCase): def testClusterAllocationSampleSimple1ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], True) def testClusterAllocationSampleSimple1WrongInitials1ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [1, 2, 3, 4], [2, 2, 3, 3], True) def testClusterAllocationSampleSimple1DistanceMatrixByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], True, data_type='distance_matrix') def testClusterAllocationSampleSimple1EuclideanByCore(self): metric = distance_metric(type_metric.EUCLIDEAN) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1EuclideanDistanceMatrixByCore(self): metric = distance_metric(type_metric.EUCLIDEAN) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1SquareEuclideanByCore(self): metric = distance_metric(type_metric.EUCLIDEAN_SQUARE) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1SquareEuclideanDistanceMatrixByCore(self): metric = distance_metric(type_metric.EUCLIDEAN_SQUARE) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1ManhattanByCore(self): metric = distance_metric(type_metric.MANHATTAN) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1ManhattanDistanceMatrixByCore(self): metric = distance_metric(type_metric.MANHATTAN) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1ChebyshevByCore(self): metric = distance_metric(type_metric.CHEBYSHEV) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1ChebyshevDistanceMatrixByCore(self): metric = distance_metric(type_metric.CHEBYSHEV) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1MinkowskiByCore(self): metric = distance_metric(type_metric.MINKOWSKI, degree=2.0) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1MinkowskiDistanceMatrixByCore(self): metric = distance_metric(type_metric.MINKOWSKI, degree=2.0) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1GowerByCore(self): metric = distance_metric(type_metric.GOWER, data=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1GowerDistanceMatrixByCore(self): metric = distance_metric(type_metric.GOWER, data=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterAllocationSampleSimple1UserDefinedByCore(self): metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True) def testClusterAllocationSampleSimple1UserDefinedDistanceMatrixByCore(self): metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, True, data_type='distance_matrix') def testClusterOneAllocationSampleSimple1ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [5], [10], True) def testClusterOneAllocationSampleSimple1DistanceMatrixByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [5], [10], True, data_type='distance_matrix') def testClusterAllocationSampleSimple2ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], True) def testClusterAllocationSampleSimple2DistanceMatrixByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], True, data_type='distance_matrix') def testClusterOneAllocationSampleSimple2ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [10], [23], True) def testClusterOneAllocationSampleSimple2DistanceMatrixByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [10], [23], True, data_type='distance_matrix') def testClusterAllocationSampleSimple3ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [4, 12, 25, 37], [10, 10, 10, 30], True) def testClusterOneAllocationSampleSimple3ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [30], [60], True) def testClusterAllocationSampleSimple5ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [4, 18, 34, 55], [15, 15, 15, 15], True) def testClusterOneAllocationSampleSimple5ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [35], [60], True) def testClusterTheSameData1ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, [2, 20], [10, 20], True) def testClusterTheSameData2ByCore(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, [2, 7, 12], [5, 5, 5], True) def testClusterAllocationTheSameObjectsOneInitialMedoidByCore(self): kmedoids_test_template.templateClusterAllocationTheSameObjects(20, 1, True) def testClusterAllocationTheSameObjectsTwoInitialMedoidsByCore(self): kmedoids_test_template.templateClusterAllocationTheSameObjects(15, 2, True) def testClusterAllocationTheSameObjectsThreeInitialMedoidsByCore(self): kmedoids_test_template.templateClusterAllocationTheSameObjects(25, 3, True) def testCoreInterfaceIntInputData(self): kmedoids_instance = kmedoids([[1], [2], [3], [20], [21], [22]], [2, 5], 0.025, True) kmedoids_instance.process() assert len(kmedoids_instance.get_clusters()) == 2 def testAllocatedRequestedClustersSampleSimple04ByCore(self): sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 10, None, True) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 25, None, True) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 40, None, True) def testAllocatedRequestedClustersWithTheSamePointsByCore(self): # Bug issue #366 - Kmedoids returns incorrect number of clusters. sample = [[0.0, 0.0], [0.1, 0.1], [0.0, 0.0], [0.1, 0.2]] kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, True) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, True) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 2, None, True) kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, True) def testAllocatedRequestedClustersWithTheSamePoints2(self): sample = [[0.23, 0.2], [-0.1, 0.1], [0.0, 0.9], [0.1, -0.2], [0.8, 0.1], [-0.1, 0.1], [-0.4, -0.2], [0.0, 0.9]] answers = [1, 2, 3, 4, 5, 6, 6, 6] for expected_amount in answers: kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, expected_amount, None, True) def testAllocatedRequestedClustersWithTotallyTheSamePointsByCore(self): # Bug issue #366 - Kmedoids returns incorrect number of clusters. sample = [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]] kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, True) @remove_library def testProcessingWhenLibraryCoreCorrupted(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], True) def testItermax0(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [], True, itermax=0) def testItermax1(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], True, itermax=1) def testItermax10Simple01(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], True, itermax=10) def testItermax10Simple02(self): kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], True, itermax=10) def testSimple01AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, SIMPLE_ANSWERS.ANSWER_SIMPLE1, True, random_state=1000) def testSimple01AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, SIMPLE_ANSWERS.ANSWER_SIMPLE1, True, random_state=1000, data_type='distance_matrix') def testSimple02AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, SIMPLE_ANSWERS.ANSWER_SIMPLE2, True, random_state=1000) def testSimple02AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, SIMPLE_ANSWERS.ANSWER_SIMPLE2, True, random_state=1000, data_type='distance_matrix') def testSimple03AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, SIMPLE_ANSWERS.ANSWER_SIMPLE3, True, random_state=1000) def testSimple03AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, SIMPLE_ANSWERS.ANSWER_SIMPLE3, True, random_state=1000, data_type='distance_matrix') def testSimple04AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, SIMPLE_ANSWERS.ANSWER_SIMPLE4, True, random_state=1000) def testSimple04AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, SIMPLE_ANSWERS.ANSWER_SIMPLE4, True, random_state=1000, data_type='distance_matrix') def testSimple05AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, SIMPLE_ANSWERS.ANSWER_SIMPLE5, True, random_state=1000) def testSimple05AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, SIMPLE_ANSWERS.ANSWER_SIMPLE5, True, random_state=1000, data_type='distance_matrix') def testSimple06AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE6, SIMPLE_ANSWERS.ANSWER_SIMPLE6, True, random_state=1000) def testSimple06AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE6, SIMPLE_ANSWERS.ANSWER_SIMPLE6, True, random_state=1000, data_type='distance_matrix') def testSimple07AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, SIMPLE_ANSWERS.ANSWER_SIMPLE7, True, random_state=1000) def testSimple07AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, SIMPLE_ANSWERS.ANSWER_SIMPLE7, True, random_state=1000, data_type='distance_matrix') def testSimple08AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, SIMPLE_ANSWERS.ANSWER_SIMPLE8, True, random_state=1000) def testSimple08AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, SIMPLE_ANSWERS.ANSWER_SIMPLE8, True, random_state=1000, data_type='distance_matrix') def testSimple09AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, SIMPLE_ANSWERS.ANSWER_SIMPLE9, True, random_state=1000) def testSimple09AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, SIMPLE_ANSWERS.ANSWER_SIMPLE9, True, random_state=1000, data_type='distance_matrix') def testSimple10AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, SIMPLE_ANSWERS.ANSWER_SIMPLE10, True, random_state=1000) def testSimple10AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, SIMPLE_ANSWERS.ANSWER_SIMPLE10, True, random_state=1000, data_type='distance_matrix') def testSimple11AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, SIMPLE_ANSWERS.ANSWER_SIMPLE11, True, random_state=1000) def testSimple11AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, SIMPLE_ANSWERS.ANSWER_SIMPLE11, True, random_state=1000, data_type='distance_matrix') def testSimple12AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, SIMPLE_ANSWERS.ANSWER_SIMPLE12, True, random_state=1000) def testSimple12AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, SIMPLE_ANSWERS.ANSWER_SIMPLE12, True, random_state=1000, data_type='distance_matrix') def testSimple13AnswerByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, SIMPLE_ANSWERS.ANSWER_SIMPLE13, True, random_state=1000) def testSimple13AnswerDistanceMatrixByCore(self): kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, SIMPLE_ANSWERS.ANSWER_SIMPLE13, True, random_state=1000, data_type='distance_matrix')