Module PdmContext.ContextClustering

Classes

class DBscanContextStream (cluster_similarity_limit=0.7, min_points=2, distancefunc=None)

A simple clustering method for context (inspired from streaming DBSCAN)

Parameters:

cluster_similarity_limit: The similarity upon two clusters are merged

min_points:

distancefunc: The function to use in order to calculate two Contexts' similarity. None value results in the usage of default distance function implemented in the class. In case of user's distance function, this has to take the two contexts as parameter and

return a real number in [0,1] indicating their similarity (1 is maximum similarity)

Expand source code

class DBscanContextStream():


    def __init__(self,cluster_similarity_limit=0.7,min_points=2,distancefunc=None):
        """
        A simple clustering method for context (inspired from streaming DBSCAN)

        Parameters:

        cluster_similarity_limit: The similarity upon two clusters are merged

        min_points:

        distancefunc: The function to use in order to calculate two Contexts' similarity. None value results in
        the usage of default distance function implemented in the class.
        In case of user's distance function, this has to take the two contexts as parameter and

        return a real number in [0,1] indicating their similarity (1 is maximum similarity)
        """

        self.cluster_similarity_limit=cluster_similarity_limit
        self.contexts={}
        self.min_points=min_points
        self.index= {}
        self.label= {}
        self.a=0.5
        self.b=1-self.a
        self.clusters_sets=[]
        self.count=-1
        self.D=[]
        self.Co=[] # coexist

        if distancefunc is None:
            self.distanceTouse=self.distance_default
        else:
            self.distanceTouse = distancefunc

    def cluster_is_similar(self, cluster, id):
        for j in cluster:
            if self.distance(id, j) > self.cluster_similarity_limit:
                return True
        return False

    def distance(self,id1,id2):
        return self.distanceTouse(self.contexts[id1],self.contexts[id2])

    def distance_default(self,c1,c2):
        return distances.distance_cc(c1, c2, self.a, self.b)[0]

    def add_sample_to_cluster(self, context : Context):

        self.count+=1
        self.index[context.timestamp]=self.count
        self.contexts[self.count] = context
        #self.D.append([ self.context_ob.distance_cc(context,self.contexts[i])[0] for i in range(self.count)])
        #self.Co.append([ 1 if self.context_ob.distance_cc(context,self.contexts[i])[0]>self.cluster_similarity_limit else 0 for i in range(self.count)])
        if len(self.clusters_sets)==0:
            self.clusters_sets.append([self.count])
        else:
            similarclusters=[]
            for i,clust in enumerate(self.clusters_sets):
                belong=self.cluster_is_similar(clust, self.count)
                if belong:
                    similarclusters.append(i)
            if len(similarclusters)==0:
                self.clusters_sets.append([self.count])
            elif len(similarclusters)==1:
                self.clusters_sets[similarclusters[0]].append(self.count)
            else:
                new_clusters=[]
                merge_cluster=[]
                for i,clust in enumerate(self.clusters_sets):
                    if i in similarclusters:
                        merge_cluster.extend(clust)
                    else:
                        new_clusters.append(clust)
                new_clusters.append(merge_cluster)
                self.clusters_sets=new_clusters


    # check if timestamp_target has in its neighborhood one of timestamps_query
    def efficient_neighbor(self,timestamp_target,timestamps_query):
        # get the cluster of target:
        idtarget=self.index[timestamp_target]
        cluserid=-1
        for i in range(len(self.clusters_sets)):
            if idtarget in self.clusters_sets[i]:
                cluserid=i
                break
        if cluserid==-1:
            return False,0
        all_query=timestamps_query.copy()
        in_same_cluster=[]
        for id in self.clusters_sets[cluserid]:
            if self.contexts[id].timestamp in all_query:
                in_same_cluster.append(id)
                all_query.remove(self.contexts[id].timestamp)

        if len(in_same_cluster)==0:
            return False,0

        for id in in_same_cluster:
            dist=self.distance(id,idtarget)
            if dist>self.cluster_similarity_limit:
                return True,dist
        return False,0
    def plot(self):
        num=1
        noisyclust=[]
        for clust in self.clusters_sets:
            if len(clust)<=self.min_points:
                noisyclust.extend(clust)
                continue
            self.plotsingleclust(num,clust)
            num+=1

        self.plotsingleclust(num, noisyclust,"noise")
        plt.show()


    def plotsingleclust(self,num,clust,desc=None):
        xaxistimes = [self.contexts[id].timestamp for id in clust]
        yaxistimes = [num for id in clust]
        if len(xaxistimes)==0:
            return
        edges = [self.contexts[id].CR["edges"] for id in clust]
        if len(edges)!=0:
            edgeplot = edges[len(edges) // 2]
        else:
            edgeplot=[]
        toplot = ""
        seen = []
        for ed in edgeplot:
            if (ed[0], ed[1]) not in seen:
                toplot += f"({ed[0]},{ed[1]}),"
                seen.append((ed[1], ed[0]))
                seen.append((ed[0], ed[1]))
        if desc is None:
            plt.text(xaxistimes[len(xaxistimes) // 2], yaxistimes[len(yaxistimes) // 2] + 0.1, toplot, fontsize=8)
        else:
            plt.text(xaxistimes[len(xaxistimes) // 2], yaxistimes[len(yaxistimes) // 2] + 0.1, desc, fontsize=8)

        plt.scatter(xaxistimes, yaxistimes)

Methods

def add_sample_to_cluster(self, context: Context)

Expand source code

def add_sample_to_cluster(self, context : Context):

    self.count+=1
    self.index[context.timestamp]=self.count
    self.contexts[self.count] = context
    #self.D.append([ self.context_ob.distance_cc(context,self.contexts[i])[0] for i in range(self.count)])
    #self.Co.append([ 1 if self.context_ob.distance_cc(context,self.contexts[i])[0]>self.cluster_similarity_limit else 0 for i in range(self.count)])
    if len(self.clusters_sets)==0:
        self.clusters_sets.append([self.count])
    else:
        similarclusters=[]
        for i,clust in enumerate(self.clusters_sets):
            belong=self.cluster_is_similar(clust, self.count)
            if belong:
                similarclusters.append(i)
        if len(similarclusters)==0:
            self.clusters_sets.append([self.count])
        elif len(similarclusters)==1:
            self.clusters_sets[similarclusters[0]].append(self.count)
        else:
            new_clusters=[]
            merge_cluster=[]
            for i,clust in enumerate(self.clusters_sets):
                if i in similarclusters:
                    merge_cluster.extend(clust)
                else:
                    new_clusters.append(clust)
            new_clusters.append(merge_cluster)
            self.clusters_sets=new_clusters

def cluster_is_similar(self, cluster, id)

Expand source code

def cluster_is_similar(self, cluster, id):
    for j in cluster:
        if self.distance(id, j) > self.cluster_similarity_limit:
            return True
    return False

def distance(self, id1, id2)

Expand source code

def distance(self,id1,id2):
    return self.distanceTouse(self.contexts[id1],self.contexts[id2])

def distance_default(self, c1, c2)

Expand source code

def distance_default(self,c1,c2):
    return distances.distance_cc(c1, c2, self.a, self.b)[0]

def efficient_neighbor(self, timestamp_target, timestamps_query)

Expand source code

def efficient_neighbor(self,timestamp_target,timestamps_query):
    # get the cluster of target:
    idtarget=self.index[timestamp_target]
    cluserid=-1
    for i in range(len(self.clusters_sets)):
        if idtarget in self.clusters_sets[i]:
            cluserid=i
            break
    if cluserid==-1:
        return False,0
    all_query=timestamps_query.copy()
    in_same_cluster=[]
    for id in self.clusters_sets[cluserid]:
        if self.contexts[id].timestamp in all_query:
            in_same_cluster.append(id)
            all_query.remove(self.contexts[id].timestamp)

    if len(in_same_cluster)==0:
        return False,0

    for id in in_same_cluster:
        dist=self.distance(id,idtarget)
        if dist>self.cluster_similarity_limit:
            return True,dist
    return False,0

def plot(self)

Expand source code

def plot(self):
    num=1
    noisyclust=[]
    for clust in self.clusters_sets:
        if len(clust)<=self.min_points:
            noisyclust.extend(clust)
            continue
        self.plotsingleclust(num,clust)
        num+=1

    self.plotsingleclust(num, noisyclust,"noise")
    plt.show()

def plotsingleclust(self, num, clust, desc=None)

Expand source code

def plotsingleclust(self,num,clust,desc=None):
    xaxistimes = [self.contexts[id].timestamp for id in clust]
    yaxistimes = [num for id in clust]
    if len(xaxistimes)==0:
        return
    edges = [self.contexts[id].CR["edges"] for id in clust]
    if len(edges)!=0:
        edgeplot = edges[len(edges) // 2]
    else:
        edgeplot=[]
    toplot = ""
    seen = []
    for ed in edgeplot:
        if (ed[0], ed[1]) not in seen:
            toplot += f"({ed[0]},{ed[1]}),"
            seen.append((ed[1], ed[0]))
            seen.append((ed[0], ed[1]))
    if desc is None:
        plt.text(xaxistimes[len(xaxistimes) // 2], yaxistimes[len(yaxistimes) // 2] + 0.1, toplot, fontsize=8)
    else:
        plt.text(xaxistimes[len(xaxistimes) // 2], yaxistimes[len(yaxistimes) // 2] + 0.1, desc, fontsize=8)

    plt.scatter(xaxistimes, yaxistimes)