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"""!
@brief Test templates for K-Medoids clustering module.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import math
import numpy
from pyclustering.tests.assertion import assertion
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.samples import answer_reader
from pyclustering.utils import read_sample, calculate_distance_matrix
from pyclustering.utils.metric import distance_metric, type_metric
from random import random, randint
class kmedoids_test_template:
@staticmethod
def templateLengthProcessData(path_to_file, initial_medoids, expected_cluster_length, ccore_flag, **kwargs):
kmedoids_test_template.templateLengthProcessWithMetric(path_to_file, initial_medoids, expected_cluster_length, None, ccore_flag, **kwargs)
@staticmethod
def templateLengthProcessWithMetric(path_to_file, initial_medoids, expected_cluster_length, metric, ccore_flag, **kwargs):
sample = read_sample(path_to_file)
data_type = kwargs.get('data_type', 'points')
input_type = kwargs.get('input_type', 'list')
initialize_medoids = kwargs.get('initialize_medoids', None)
itermax = kwargs.get('itermax', 200)
if metric is None:
metric = distance_metric(type_metric.EUCLIDEAN_SQUARE)
input_data = sample
if data_type == 'distance_matrix':
input_data = calculate_distance_matrix(sample)
if input_type == 'numpy':
input_data = numpy.array(input_data)
testing_result = False
testing_attempts = 1
if initialize_medoids is not None: # in case center initializer randomization appears
testing_attempts = 10
for _ in range(testing_attempts):
if initialize_medoids is not None:
initial_medoids = kmeans_plusplus_initializer(sample, initialize_medoids).initialize(return_index=True)
kmedoids_instance = kmedoids(input_data, initial_medoids, 0.001, ccore=ccore_flag, metric=metric, data_type=data_type, itermax=itermax)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
medoids = kmedoids_instance.get_medoids()
if itermax == 0:
assertion.eq([], clusters)
assertion.eq(medoids, initial_medoids)
return
if len(clusters) != len(medoids):
continue
if len(set(medoids)) != len(medoids):
continue
obtained_cluster_sizes = [len(cluster) for cluster in clusters]
if len(sample) != sum(obtained_cluster_sizes):
continue
for cluster in clusters:
if len(cluster) == 0:
continue
if expected_cluster_length is not None:
obtained_cluster_sizes.sort()
expected_cluster_length.sort()
if obtained_cluster_sizes != expected_cluster_length:
continue
testing_result = True
assertion.true(testing_result)
@staticmethod
def templateClusterAllocationOneDimensionData(ccore_flag):
input_data = [[random()] for _ in range(10)] + [[random() + 3] for _ in range(10)] + [[random() + 5] for _ in range(10)] + [[random() + 8] for _ in range(10)]
kmedoids_instance = kmedoids(input_data, [5, 15, 25, 35], 0.025, ccore_flag)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
assertion.eq(4, len(clusters))
for cluster in clusters:
assertion.eq(10, len(cluster))
@staticmethod
def templateAllocateRequestedClusterAmount(data, amount_clusters, initial_medoids, ccore_flag):
if initial_medoids is None:
initial_medoids = []
for _ in range(amount_clusters):
index_point = randint(0, len(data) - 1)
while index_point in initial_medoids:
index_point = randint(0, len(data) - 1)
initial_medoids.append(index_point)
kmedoids_instance = kmedoids(data, initial_medoids, 0.025, ccore=ccore_flag)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
assertion.eq(len(clusters), amount_clusters)
amount_objects = 0
for cluster in clusters:
amount_objects += len(cluster)
assertion.eq(len(data), amount_objects)
@staticmethod
def templateClusterAllocationTheSameObjects(number_objects, number_clusters, ccore_flag=False):
value = random()
input_data = [[value]] * number_objects
initial_medoids = []
step = int(math.floor(number_objects / number_clusters))
for i in range(number_clusters):
initial_medoids.append(i * step)
kmedoids_instance = kmedoids(input_data, initial_medoids, ccore=ccore_flag)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
medoids = kmedoids_instance.get_medoids()
assertion.eq(len(clusters), len(medoids))
assertion.eq(len(set(medoids)), len(medoids))
object_mark = [False] * number_objects
allocated_number_objects = 0
for cluster in clusters:
for index_object in cluster:
assertion.eq(False, object_mark[index_object]) # one object can be in only one cluster.
object_mark[index_object] = True
allocated_number_objects += 1
assertion.eq(number_objects, allocated_number_objects) # number of allocated objects should be the same.
@staticmethod
def templatePredict(path_to_file, initial_medoids, points, expected_closest_clusters, ccore, **kwargs):
sample = read_sample(path_to_file)
metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE))
itermax = kwargs.get('itermax', 200)
kmedoids_instance = kmedoids(sample, initial_medoids, 0.001, ccore, metric=metric, itermax=itermax)
kmedoids_instance.process()
closest_clusters = kmedoids_instance.predict(points)
assertion.eq(len(expected_closest_clusters), len(closest_clusters))
assertion.true(numpy.array_equal(numpy.array(expected_closest_clusters), closest_clusters))
@staticmethod
def clustering_with_answer(data_file, answer_file, ccore, **kwargs):
data_type = kwargs.get('data_type', 'points')
metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN))
original_data = read_sample(data_file)
data = original_data
if data_type == 'distance_matrix':
data = calculate_distance_matrix(original_data, metric)
reader = answer_reader(answer_file)
amount_medoids = len(reader.get_clusters())
initial_medoids = kmeans_plusplus_initializer(data, amount_medoids, **kwargs).initialize(return_index=True)
kmedoids_instance = kmedoids(data, initial_medoids, 0.001, ccore, **kwargs)
kmedoids_instance.process()
clusters = kmedoids_instance.get_clusters()
medoids = kmedoids_instance.get_medoids()
expected_length_clusters = sorted(reader.get_cluster_lengths())
assertion.eq(len(expected_length_clusters), len(medoids))
assertion.eq(len(data), sum([len(cluster) for cluster in clusters]))
assertion.eq(sum(expected_length_clusters), sum([len(cluster) for cluster in clusters]))
unique_medoids = set()
for medoid in medoids:
assertion.false(medoid in unique_medoids, message="Medoids '%s' is not unique (actual medoids: '%s')" % (str(medoid), str(unique_medoids)))
unique_medoids.add(medoid)
unique_points = set()
for cluster in clusters:
for point in cluster:
assertion.false(point in unique_points, message="Point '%s' is already assigned to one of the clusters." % str(point))
unique_points.add(point)
assertion.eq(expected_length_clusters, sorted([len(cluster) for cluster in clusters]))
expected_clusters = reader.get_clusters()
for actual_cluster in clusters:
cluster_found = False
for expected_cluster in expected_clusters:
if actual_cluster == expected_cluster:
cluster_found = True
assertion.true(cluster_found, message="Actual cluster '%s' is not found among expected." % str(actual_cluster))
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