optics.py

"""!
 
@brief Cluster analysis algorithm: OPTICS (Ordering Points To Identify Clustering Structure)
@details Implementation based on paper @cite article::optics::1.
 
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
 
"""
 
 
import math
import matplotlib.pyplot as plt
 
from pyclustering.container.kdtree import kdtree_balanced
 
from pyclustering.cluster.encoder import type_encoding
 
from pyclustering.utils.color import color as color_list
 
from pyclustering.core.wrapper import ccore_library
 
import pyclustering.core.optics_wrapper as wrapper
 
 
class ordering_visualizer:
    """!
    @brief Cluster ordering diagram visualizer that represents dataset graphically as density-based clustering structure.
    @details This OPTICS algorithm is KD-tree optimized.
    
    @see ordering_analyser
    
    """
    
    @staticmethod
    def show_ordering_diagram(analyser, amount_clusters = None):
        """!
        @brief Display cluster-ordering (reachability-plot) diagram.
        
        @param[in] analyser (ordering_analyser): cluster-ordering analyser whose ordering diagram should be displayed.
        @param[in] amount_clusters (uint): if it is not 'None' then it displays connectivity radius line that can used for allocation of specified amount of clusters
                    and colorize diagram by corresponding cluster colors.
        
        Example demonstrates general abilities of 'ordering_visualizer' class:
        @code
            # Display cluster-ordering diagram with connectivity radius is used for allocation of three clusters.
            ordering_visualizer.show_ordering_diagram(analyser, 3);
        
            # Display cluster-ordering diagram without radius.
            ordering_visualizer.show_ordering_diagram(analyser);
        @endcode
        
        """
        ordering = analyser.cluster_ordering
        axis = plt.subplot(111)
        
        if amount_clusters is not None:
            radius, borders = analyser.calculate_connvectivity_radius(amount_clusters)
        
            # divide into cluster groups to visualize by colors
            left_index_border = 0
            current_index_border = 0
            for index_border in range(len(borders)):
                right_index_border = borders[index_border]
                axis.bar(range(left_index_border, right_index_border), ordering[left_index_border:right_index_border], width = 1.0, color = color_list.TITLES[index_border])
                left_index_border = right_index_border
                current_index_border = index_border
            
            axis.bar(range(left_index_border, len(ordering)), ordering[left_index_border:len(ordering)], width = 1.0, color = color_list.TITLES[current_index_border + 1])
            
            plt.xlim([0, len(ordering)])
            
            plt.axhline(y = radius, linewidth = 2, color = 'black')
            plt.text(0, radius + radius * 0.03, " Radius:   " + str(round(radius, 4)) + ";\n Clusters: " + str(amount_clusters), color = 'b', fontsize = 10)
            
        else:
            axis.bar(range(0, len(ordering)), ordering[0:len(ordering)], width = 1.0, color = 'black')
            plt.xlim([0, len(ordering)])
        
        plt.show()
 
 
class ordering_analyser:
    """!
    @brief Analyser of cluster ordering diagram.
    @details Using cluster-ordering it is able to connectivity radius for allocation of specified amount of clusters and
              calculate amount of clusters using specified connectivity radius. Cluster-ordering is formed by OPTICS algorithm
              during cluster analysis.
    
    @see optics
    
    """
    
    @property
    def cluster_ordering(self):
        """!
        @brief (list) Returns values of dataset cluster ordering.
        
        """
        return self.__ordering
    
    
    def __init__(self, ordering_diagram):
        """!
        @brief Analyser of ordering diagram that is based on reachability-distances.
        
        @see calculate_connvectivity_radius
        
        """
        self.__ordering = ordering_diagram
    
    
    def __len__(self):
        """!
        @brief Returns length of clustering-ordering diagram.
        
        """
        return len(self.__ordering)
    
    
    def calculate_connvectivity_radius(self, amount_clusters, maximum_iterations = 100):
        """!
        @brief Calculates connectivity radius of allocation specified amount of clusters using ordering diagram and marks borders of clusters using indexes of values of ordering diagram.
        @details Parameter 'maximum_iterations' is used to protect from hanging when it is impossible to allocate specified number of clusters.
        
        @param[in] amount_clusters (uint): amount of clusters that should be allocated by calculated connectivity radius.
        @param[in] maximum_iterations (uint): maximum number of iteration for searching connectivity radius to allocated specified amount of clusters (by default it is restricted by 100 iterations).
        
        @return (double, list) Value of connectivity radius and borders of clusters like (radius, borders), radius may be 'None' as well as borders may be '[]'
                                if connectivity radius hasn't been found for the specified amount of iterations.
        
        """
        
        maximum_distance = max(self.__ordering)
        
        upper_distance = maximum_distance
        lower_distance = 0.0
 
        result = None
        
        amount, borders = self.extract_cluster_amount(maximum_distance)
        if amount <= amount_clusters:
            for _ in range(maximum_iterations):
                radius = (lower_distance + upper_distance) / 2.0
                
                amount, borders = self.extract_cluster_amount(radius)
                if amount == amount_clusters:
                    result = radius
                    break
                
                elif amount == 0:
                    break
                
                elif amount > amount_clusters:
                    lower_distance = radius
                
                elif amount < amount_clusters:
                    upper_distance = radius
        
        return result, borders
    
    
    def extract_cluster_amount(self, radius):
        """!
        @brief Obtains amount of clustering that can be allocated by using specified radius for ordering diagram and borders between them.
        @details When growth of reachability-distances is detected than it is considered as a start point of cluster, 
                 than pick is detected and after that recession is observed until new growth (that means end of the
                 current cluster and start of a new one) or end of diagram.
        
        @param[in] radius (double): connectivity radius that is used for cluster allocation.
        
        @return (unit, list) Amount of clusters that can be allocated by the connectivity radius on ordering diagram and borders between them using indexes
                 from ordering diagram (amount_clusters, border_clusters).
        
        """
        
        amount_clusters = 1
        
        cluster_start = False
        cluster_pick = False
        total_similarity = True
        previous_cluster_distance = None
        previous_distance = None
        
        cluster_borders = []
        
        for index_ordering in range(len(self.__ordering)):
            distance = self.__ordering[index_ordering]
            if distance >= radius:
                if cluster_start is False:
                    cluster_start = True
                    amount_clusters += 1
                    
                    if index_ordering != 0:
                        cluster_borders.append(index_ordering)
                
                else:
                    if (distance < previous_cluster_distance) and (cluster_pick is False):
                        cluster_pick = True
                    
                    elif (distance > previous_cluster_distance) and (cluster_pick is True):
                        cluster_pick = False
                        amount_clusters += 1
                        
                        if index_ordering != 0:
                            cluster_borders.append(index_ordering)
                
                previous_cluster_distance = distance
            
            else:
                cluster_start = False
                cluster_pick = False
            
            if (previous_distance is not None) and (distance != previous_distance):
                total_similarity = False
            
            previous_distance = distance
        
        if (total_similarity is True) and (previous_distance > radius):
            amount_clusters = 0
 
        return amount_clusters, cluster_borders
 
 
class optics_descriptor:
    """!
    @brief Object description that used by OPTICS algorithm for cluster analysis.
    
    """
 
    def __init__(self, index, core_distance = None, reachability_distance = None):
        """!
        @brief Constructor of object description in optics terms.
        
        @param[in] index (uint): Index of the object in the data set.
        @param[in] core_distance (double): Core distance that is minimum distance to specified number of neighbors.
        @param[in] reachability_distance (double): Reachability distance to this object.
        
        """
 
        
        self.index_object = index
        
        
        self.core_distance = core_distance
 
        
        self.reachability_distance = reachability_distance
        
        
        self.processed = False
 
    def __repr__(self):
        """!
        @brief Returns string representation of the optics descriptor.
        
        """
        
        return '(%s, [c: %s, r: %s])' % (self.index_object, self.core_distance, self.reachability_distance)
 
 
class optics:
    """!
    @brief Class represents clustering algorithm OPTICS (Ordering Points To Identify Clustering Structure) with KD-tree optimization (ccore options is supported).
    @details OPTICS is a density-based algorithm. Purpose of the algorithm is to provide explicit clusters, but create clustering-ordering representation of the input data. 
             Clustering-ordering information contains information about internal structures of data set in terms of density and proper connectivity radius can be obtained
             for allocation required amount of clusters using this diagram. In case of usage additional input parameter 'amount of clusters' connectivity radius should be
             bigger than real - because it will be calculated by the algorithms if requested amount of clusters is not allocated.
 
    @image html optics_example_clustering.png "Scheme how does OPTICS works. At the beginning only one cluster is allocated, but two is requested. At the second step OPTICS calculates connectivity radius using cluster-ordering and performs final cluster allocation."
 
    Clustering example using sample 'Chainlink':
    @code
        from pyclustering.cluster import cluster_visualizer
        from pyclustering.cluster.optics import optics, ordering_analyser, ordering_visualizer
        from pyclustering.samples.definitions import FCPS_SAMPLES
        from pyclustering.utils import read_sample
 
        # Read sample for clustering from some file.
        sample = read_sample(FCPS_SAMPLES.SAMPLE_CHAINLINK)
 
        # Run cluster analysis where connectivity radius is bigger than real.
        radius = 0.5
        neighbors = 3
        optics_instance = optics(sample, radius, neighbors)
 
        # Performs cluster analysis.
        optics_instance.process()
 
        # Obtain results of clustering.
        clusters = optics_instance.get_clusters()
        noise = optics_instance.get_noise()
        ordering = optics_instance.get_ordering()
 
        # Visualize clustering results.
        visualizer = cluster_visualizer()
        visualizer.append_clusters(clusters, sample)
        visualizer.show()
 
        # Display ordering.
        analyser = ordering_analyser(ordering)
        ordering_visualizer.show_ordering_diagram(analyser, 2)
    @endcode
 
    Amount of clusters that should be allocated can be also specified. In this case connectivity radius should be greater than real, for example:
    @code
        from pyclustering.cluster import cluster_visualizer
        from pyclustering.cluster.optics import optics, ordering_analyser, ordering_visualizer
        from pyclustering.samples.definitions import FCPS_SAMPLES
        from pyclustering.utils import read_sample
 
        # Read sample for clustering from some file
        sample = read_sample(FCPS_SAMPLES.SAMPLE_LSUN)
 
        # Run cluster analysis where connectivity radius is bigger than real
        radius = 2.0
        neighbors = 3
        amount_of_clusters = 3
        optics_instance = optics(sample, radius, neighbors, amount_of_clusters)
 
        # Performs cluster analysis
        optics_instance.process()
 
        # Obtain results of clustering
        clusters = optics_instance.get_clusters()
        noise = optics_instance.get_noise()
        ordering = optics_instance.get_ordering()
 
        # Visualize ordering diagram
        analyser = ordering_analyser(ordering)
        ordering_visualizer.show_ordering_diagram(analyser, amount_of_clusters)
 
        # Visualize clustering results
        visualizer = cluster_visualizer()
        visualizer.append_clusters(clusters, sample)
        visualizer.show()
    @endcode
 
    Here is an example where OPTICS extracts outliers from sample 'Tetra':
    @code
        from pyclustering.cluster import cluster_visualizer
        from pyclustering.cluster.optics import optics
        from pyclustering.samples.definitions import FCPS_SAMPLES
        from pyclustering.utils import read_sample
 
        # Read sample for clustering from some file.
        sample = read_sample(FCPS_SAMPLES.SAMPLE_TETRA)
 
        # Run cluster analysis where connectivity radius is bigger than real.
        radius = 0.4
        neighbors = 3
        optics_instance = optics(sample, radius, neighbors)
 
        # Performs cluster analysis.
        optics_instance.process()
 
        # Obtain results of clustering.
        clusters = optics_instance.get_clusters()
        noise = optics_instance.get_noise()
 
        # Visualize clustering results (clusters and outliers).
        visualizer = cluster_visualizer()
        visualizer.append_clusters(clusters, sample)
        visualizer.append_cluster(noise, sample, marker='x')
        visualizer.show()
    @endcode
 
    Visualization result of allocated clusters and outliers is presented on the image below:
    @image html optics_noise_tetra.png "Clusters and outliers extracted by OPTICS algorithm from sample 'Tetra'."
 
    """
    
    def __init__(self, sample, eps, minpts, amount_clusters=None, ccore=True, **kwargs):
        """!
        @brief Constructor of clustering algorithm OPTICS.
        
        @param[in] sample (list): Input data that is presented as a list of points (objects), where each point is represented by list or tuple.
        @param[in] eps (double): Connectivity radius between points, points may be connected if distance between them less than the radius.
        @param[in] minpts (uint): Minimum number of shared neighbors that is required for establishing links between points.
        @param[in] amount_clusters (uint): Optional parameter where amount of clusters that should be allocated is specified.
                    In case of usage 'amount_clusters' connectivity radius can be greater than real, in other words, there is place for mistake
                    in connectivity radius usage.
        @param[in] ccore (bool): if True than DLL CCORE (C++ solution) will be used for solving the problem.
        @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'data_type').
 
        <b>Keyword Args:</b><br>
            - data_type (string): Data type of input sample 'data' that is processed by the algorithm ('points', 'distance_matrix').
 
        """
        
        self.__sample_pointer = sample      # Algorithm parameter - pointer to sample for processing.
        self.__eps = eps                    # Algorithm parameter - connectivity radius between object for establish links between object.
        self.__minpts = minpts              # Algorithm parameter - minimum number of neighbors that is required for establish links between object.
        self.__amount_clusters = amount_clusters
        
        self.__ordering = None
        self.__clusters = None
        self.__noise = None
        self.__optics_objects = None
 
        self.__data_type = kwargs.get('data_type', 'points')
        
        self.__kdtree = None
        self.__ccore = ccore
 
        self.__neighbor_searcher = self.__create_neighbor_searcher(self.__data_type)
 
        if self.__ccore:
            self.__ccore = ccore_library.workable()
 
        self.__verify_arguments()
 
 
    def process(self):
        """!
        @brief Performs cluster analysis in line with rules of OPTICS algorithm.
        
        @return (optics) Returns itself (OPTICS instance).
        
        @see get_clusters()
        @see get_noise()
        @see get_ordering()
        
        """
        
        if self.__ccore is True:
            self.__process_by_ccore()
        
        else:
            self.__process_by_python()
 
        return self
 
 
    def __process_by_ccore(self):
        """!
        @brief Performs cluster analysis using CCORE (C/C++ part of pyclustering library).
 
        """
 
        (self.__clusters, self.__noise, self.__ordering, self.__eps,
         objects_indexes, objects_core_distances, objects_reachability_distances) = \
            wrapper.optics(self.__sample_pointer, self.__eps, self.__minpts, self.__amount_clusters, self.__data_type)
 
        self.__optics_objects = []
        for i in range(len(objects_indexes)):
            if objects_core_distances[i] < 0.0:
                objects_core_distances[i] = None
 
            if objects_reachability_distances[i] < 0.0:
                objects_reachability_distances[i] = None
 
            optics_object = optics_descriptor(objects_indexes[i], objects_core_distances[i], objects_reachability_distances[i])
            optics_object.processed = True
 
            self.__optics_objects.append(optics_object)
 
 
    def __process_by_python(self):
        """!
        @brief Performs cluster analysis using python code.
 
        """
 
        if self.__data_type == 'points':
            self.__kdtree = kdtree_balanced(self.__sample_pointer, range(len(self.__sample_pointer)))
 
        self.__allocate_clusters()
 
        if (self.__amount_clusters is not None) and (self.__amount_clusters != len(self.get_clusters())):
            analyser = ordering_analyser(self.get_ordering())
            radius, _ = analyser.calculate_connvectivity_radius(self.__amount_clusters)
            if radius is not None:
                self.__eps = radius
                self.__allocate_clusters()
 
 
    def __initialize(self, sample):
        """!
        @brief Initializes internal states and resets clustering results in line with input sample.
        
        """
        
        self.__processed = [False] * len(sample)
        self.__optics_objects = [optics_descriptor(i) for i in range(len(sample))]      # List of OPTICS objects that corresponds to objects from input sample.
        self.__ordered_database = []        # List of OPTICS objects in traverse order.
        
        self.__clusters = None      # Result of clustering (list of clusters where each cluster contains indexes of objects from input data).
        self.__noise = None         # Result of clustering (noise).
 
 
    def __allocate_clusters(self):
        """!
        @brief Performs cluster allocation and builds ordering diagram that is based on reachability-distances.
        
        """
        
        self.__initialize(self.__sample_pointer)
        
        for optic_object in self.__optics_objects:
            if optic_object.processed is False:
                self.__expand_cluster_order(optic_object)
        
        self.__extract_clusters()
    
    
    def get_clusters(self):
        """!
        @brief Returns list of allocated clusters, where each cluster contains indexes of objects and each cluster is represented by list.
        
        @return (list) List of allocated clusters.
        
        @see process()
        @see get_noise()
        @see get_ordering()
        @see get_radius()
        
        """
        
        return self.__clusters
    
    
    def get_noise(self):
        """!
        @brief Returns list of noise that contains indexes of objects that corresponds to input data.
        
        @return (list) List of allocated noise objects.
        
        @see process()
        @see get_clusters()
        @see get_ordering()
        @see get_radius()
        
        """
        
        return self.__noise
    
    
    def get_ordering(self):
        """!
        @brief Returns clustering ordering information about the input data set.
        @details Clustering ordering of data-set contains the information about the internal clustering structure in line with connectivity radius.
        
        @return (ordering_analyser) Analyser of clustering ordering.
        
        @see process()
        @see get_clusters()
        @see get_noise()
        @see get_radius()
        @see get_optics_objects()
        
        """
        
        if self.__ordering is None:
            self.__ordering = []
        
            for cluster in self.__clusters:
                for index_object in cluster:
                    optics_object = self.__optics_objects[index_object]
                    if optics_object.reachability_distance is not None:
                        self.__ordering.append(optics_object.reachability_distance)
            
        return self.__ordering
 
 
    def get_optics_objects(self):
        """!
        @brief Returns OPTICS objects where each object contains information about index of point from processed data,
                core distance and reachability distance.
 
        @return (list) OPTICS objects.
 
        @see get_ordering()
        @see get_clusters()
        @see get_noise()
        @see optics_descriptor
 
        """
 
        return self.__optics_objects
 
    
    def get_radius(self):
        """!
        @brief Returns connectivity radius that is calculated and used for clustering by the algorithm.
        @details Connectivity radius may be changed only in case of usage additional parameter of the algorithm - amount of clusters for allocation.
        
        @return (double) Connectivity radius.
        
        @see get_ordering()
        @see get_clusters()
        @see get_noise()
        @see get_optics_objects()
        
        """
        
        return self.__eps
    
 
    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 __create_neighbor_searcher(self, data_type):
        """!
        @brief Returns neighbor searcher in line with data type.
 
        @param[in] data_type (string): Data type (points or distance matrix).
 
        """
        if data_type == 'points':
            return self.__neighbor_indexes_points
        elif data_type == 'distance_matrix':
            return self.__neighbor_indexes_distance_matrix
        else:
            raise TypeError("Unknown type of data is specified '%s'" % data_type)
 
 
    def __expand_cluster_order(self, optics_object):
        """!
        @brief Expand cluster order from not processed optic-object that corresponds to object from input data.
               Traverse procedure is performed until objects are reachable from core-objects in line with connectivity radius.
               Order database is updated during expanding.
               
        @param[in] optics_object (optics_descriptor): Object that hasn't been processed.
        
        """
        
        optics_object.processed = True
        
        neighbors_descriptor = self.__neighbor_searcher(optics_object)
        optics_object.reachability_distance = None
        
        self.__ordered_database.append(optics_object)
        
        # Check core distance
        if len(neighbors_descriptor) >= self.__minpts:
            neighbors_descriptor.sort(key = lambda obj: obj[1])
            optics_object.core_distance = neighbors_descriptor[self.__minpts - 1][1]
            
            # Continue processing
            order_seed = list()
            self.__update_order_seed(optics_object, neighbors_descriptor, order_seed)
            
            while len(order_seed) > 0:
                optic_descriptor = order_seed[0]
                order_seed.remove(optic_descriptor)
                
                neighbors_descriptor = self.__neighbor_searcher(optic_descriptor)
                optic_descriptor.processed = True
                
                self.__ordered_database.append(optic_descriptor)
                
                if len(neighbors_descriptor) >= self.__minpts:
                    neighbors_descriptor.sort(key = lambda obj: obj[1])
                    optic_descriptor.core_distance = neighbors_descriptor[self.__minpts - 1][1]
                    
                    self.__update_order_seed(optic_descriptor, neighbors_descriptor, order_seed)
                else:
                    optic_descriptor.core_distance = None
                    
        else:
            optics_object.core_distance = None
 
    
    def __extract_clusters(self):
        """!
        @brief Extract clusters and noise from order database.
        
        """
     
        self.__clusters = []
        self.__noise = []
 
        current_cluster = self.__noise
        for optics_object in self.__ordered_database:
            if (optics_object.reachability_distance is None) or (optics_object.reachability_distance > self.__eps):
                if (optics_object.core_distance is not None) and (optics_object.core_distance <= self.__eps):
                    self.__clusters.append([ optics_object.index_object ])
                    current_cluster = self.__clusters[-1]
                else:
                    self.__noise.append(optics_object.index_object)
            else:
                current_cluster.append(optics_object.index_object)
 
 
    def __update_order_seed(self, optic_descriptor, neighbors_descriptors, order_seed):
        """!
        @brief Update sorted list of reachable objects (from core-object) that should be processed using neighbors of core-object.
        
        @param[in] optic_descriptor (optics_descriptor): Core-object whose neighbors should be analysed.
        @param[in] neighbors_descriptors (list): List of neighbors of core-object.
        @param[in|out] order_seed (list): List of sorted object in line with reachable distance.
        
        """
        
        for neighbor_descriptor in neighbors_descriptors:
            index_neighbor = neighbor_descriptor[0]
            current_reachable_distance = neighbor_descriptor[1]
            
            if self.__optics_objects[index_neighbor].processed is not True:
                reachable_distance = max(current_reachable_distance, optic_descriptor.core_distance)
                if self.__optics_objects[index_neighbor].reachability_distance is None:
                    self.__optics_objects[index_neighbor].reachability_distance = reachable_distance
                    
                    # insert element in queue O(n) - worst case.
                    index_insertion = len(order_seed)
                    for index_seed in range(0, len(order_seed)):
                        if reachable_distance < order_seed[index_seed].reachability_distance:
                            index_insertion = index_seed
                            break
                    
                    order_seed.insert(index_insertion, self.__optics_objects[index_neighbor])
 
                else:
                    if reachable_distance < self.__optics_objects[index_neighbor].reachability_distance:
                        self.__optics_objects[index_neighbor].reachability_distance = reachable_distance
                        order_seed.sort(key=lambda obj: obj.reachability_distance)
 
 
    def __neighbor_indexes_points(self, optic_object):
        """!
        @brief Return neighbors of the specified object in case of sequence of points.
 
        @param[in] optic_object (optics_descriptor): Object for which neighbors should be returned in line with connectivity radius.
 
        @return (list) List of indexes of neighbors in line the connectivity radius.
 
        """
        kdnodes = self.__kdtree.find_nearest_dist_nodes(self.__sample_pointer[optic_object.index_object], self.__eps)
        return [[node_tuple[1].payload, math.sqrt(node_tuple[0])] for node_tuple in kdnodes if
                node_tuple[1].payload != optic_object.index_object]
 
 
    def __neighbor_indexes_distance_matrix(self, optic_object):
        """!
        @brief Return neighbors of the specified object in case of distance matrix.
 
        @param[in] optic_object (optics_descriptor): Object for which neighbors should be returned in line with connectivity radius.
 
        @return (list) List of indexes of neighbors in line the connectivity radius.
 
        """
        distances = self.__sample_pointer[optic_object.index_object]
        return [[index_neighbor, distances[index_neighbor]] for index_neighbor in range(len(distances))
                if ((distances[index_neighbor] <= self.__eps) and (index_neighbor != optic_object.index_object))]
 
 
    def __verify_arguments(self):
        """!
        @brief Verify input parameters for the algorithm and throw exception in case of incorrectness.
 
        """
        if len(self.__sample_pointer) == 0:
            raise ValueError("Input data is empty (size: '%d')." % len(self.__sample_pointer))
 
        if self.__eps < 0:
            raise ValueError("Connectivity radius (current value: '%d') should be greater or equal to 0." % self.__eps)
 
        if self.__minpts < 0:
            raise ValueError("Minimum number of neighbors (current value: '%d') should be greater than 0." %
                             self.__minpts)
 
        if (self.__amount_clusters is not None) and (self.__amount_clusters <= 0):
            raise ValueError("Amount of clusters (current value: '%d') should be greater than 0." %
                             self.__amount_clusters)