new functionalities for High Dimensionality problem and improved performance

AUTHORS

@@ -1,2 +1,6 @@
 Michel Albert (exhuma@users.sourceforge.net)
-Sam Sandberg (@LoisaidaSam)
+Sam Sandberg (@LoisaidaSam)
+
+high dimensionality functionalities:
+Jose J. GarciaAranda (@jjaranda13)
+Juan Ramos Diaz (@juanrd0088) 

HDexample.py

@@ -0,0 +1,131 @@
+# -*- coding: cp1252 -*-
+###############################################################################
+#             High Dimensionality problem example                     
+# Authors:
+#  2015 Jose Javier Garcia Aranda , Juan Ramos Diaz
+#
+###############################################################################
+# This High Dimensionality example creates N items (which are "users").
+# Each user is defined by his profile. 
+# A profile is a tuple of 10 pairs of keyword and weight ( 20 fields in total)
+# weights are floating numbers and belong to 0..1
+# The summation of weights of a profile is normalized to 1
+# we consider 1000 diferent keywords
+# A profile takes 8 keywords from first 200 keywords (the "popular" keywords)
+# Each keyword is a dimension. Therefore there are 1000 possible dimensions
+# A single user only have 10 dimensions
+# Different users can have different dimensions.
+# A new distance and equality function are defined for this use case
+#
+#     cl = KMeansClustering(users,HDdistItems,HDequals);
+#
+# Additionally, now the number of iterations can be limited in order to save time
+# Experimentally, we have concluded that 10 iterations is  enough accurate for most cases.
+# The new HDgetClusters() function is linear. Avoid the recalculation of centroids
+# whereas original function getClusters() is N*N complex, because recalculate the
+# centroid when move an item from one cluster to another. 
+# This new function can be used for low and high dimensionality problems, increasing 
+# performance in both cases
+#
+# solution = cl.HDgetclusters(numclusters,max_iterations);
+#
+# Other new available optimization inside HDcentroid() function in is the use of mean instead median at centroid calculation.
+# median is more accurate but involves more computations when N is huge. 
+# The function HDcentroid() is invoked internally by HDgetclusters()
+#
+# The optional invocation of HDcomputeSSE() assist the computation of the optimal number or clusters.
+#
+#
+from cluster import KMeansClustering
+from cluster import ClusteringError
+from cluster import util
+from cluster.util import HDcentroid
+from cluster.HDdistances import HDdistItems, HDequals, HDcomputeSSE
+import time
+import datetime
+
+import random
+
+def createProfile():
+    num_words=1000
+    total_weight=0;
+    marked_word=[0]*num_words
+    repeated_word=False
+    list_profile=[]    
+    returned_profile=();
+    profile_aux=[];
+    #10 pairs word, weight.
+    #Don't repeated words.
+    for i in range(8):
+        partial_weight=random.uniform(0,1)
+        total_weight+=partial_weight
+        repeated_word=False
+        while repeated_word==False:
+            random_word=random.randint(0,299)
+            if marked_word[random_word]==0:
+                marked_word[random_word]=1
+                repeated_word=True
+        random_word= str(random_word)
+        tupla=[random_word,partial_weight]
+        list_profile.append(tupla)
+    for i in range(2):
+        partial_weight=random.uniform(0,1)
+        total_weight+=partial_weight
+        repeated_word=False
+        while repeated_word==False:
+            random_word=random.randint(300,999)
+            if marked_word[random_word]==0:
+                marked_word[random_word]=1
+                repeated_word=True
+        random_word= str(random_word)
+        tupla=[random_word,partial_weight]
+        list_profile.append(tupla)
+    #Normalization of the profile
+    for i in range(5):
+        a=list_profile[i][0]
+        b=list_profile[i][1]
+        b=b/total_weight; #the sum of the weights must be 1       
+        profile_aux=([a,b])
+        returned_profile+=tuple(profile_aux)
+    return returned_profile
+
+####################################################
+#                    MAIN                          #
+####################################################
+sses=[0]*10 #stores the sse metric for each number of clusters from 5 to 50
+num_users=100
+numsse=0
+numclusters=5 # starts at 5
+max_iteraciones=10
+ts = time.time()
+start_time=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
+while numclusters<=50: # compute SSE from num_clusters=5 to 50
+    supersol=0#supersolucion, distancias entre el clusters y los usuarios.
+    users=[] # users are the items of this example
+    for i in range(num_users):#en el range el numero de usuarios
+        user = createProfile()
+        users.append(user)
+    #x=0;
+    print " inicializing kmeans..."
+    cl = KMeansClustering(users,HDdistItems,HDequals);
+    print " executing...",numclusters
+    ts = time.time()
+    st=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
+    print st
+    numclusters=numclusters
+    solution = cl.HDgetclusters(numclusters,max_iteraciones);
+    for i in range(numclusters):
+        a = solution[i]
+        print util.HDcentroid(a),","
+    ts = time.time()
+    st=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
+
+    sses[numsse]=HDcomputeSSE(solution,numclusters) 
+    numsse+=1
+    numclusters+=5
+ts = time.time()
+end_time=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
+print "start_time:",start_time
+print "end_time:",end_time
+print "sses:",sses
+

README.rst

@@ -53,3 +53,51 @@ The parameter passed to getclusters is the count of clusters generated.
 .. image:: https://readthedocs.org/projects/python-cluster/badge/?version=latest
     :target: http://python-cluster.readthedocs.org
     :alt: Documentation Status
+
+
+
+2015/07/20 NEW FUNCTIONALITIES FOR HIGH AND LOW DIMENSIONALITY PROBLEMS
+=======================================================================
+Authors of new added functionalities:
+  - Garcia Aranda, Jose Javier	jose_javier.garcia_aranda@alcatel-lucent.com
+  - Ramos Diaz, Juan		juanrd0088@gmail.com
+
+Acknoledgements:
+  Authors want to thank the Spanish Economy & competitiveness Ministry which funds this research 
+  through "INNPACTO" innovation program IPT-2012-0839-430000.
+
+
+High dimensionality (HD) problems are those which have items with high number of dimensions
+There are two types of HD problems::
+ a)set of items with large number of dimensions.
+ b)set of items with a limited number of dimensions from a large available number of dimensions
+  For example considering dimensions X, Y, Z, K, L, M and the items:
+    item1=(X=2, Z=5, L=7)
+    item2=(X=6, Y=5, M=7)
+
+The HD problems involves a high cost computation because distance functions in this case takes more
+operations than Low dimensionality problems.
+
+For case "b" (valid also for "a"), a new distance for HD problems is available:  HDdistItems() ,HDequals()
+This distance function compares dimensions between 2 items.
+Each dimension of item1 is searched in item2, and if it is found, then the distance takes into account the difference (manhattan style). If the dimension does not exist in item2, a maximum value is added to the total distance between item1 and item2
+
+There is no difference with current usage::
+
+ >>>cl = KMeansClustering(users,HDdistItems,HDequals);
+
+
+Additionally, now the number of iterations can be limited in order to save time
+Experimentally, we have concluded that 10 iterations is  enough accurate for most cases.
+The new HDgetClusters() function is linear. Avoid the recalculation of centroids
+whereas original function getClusters() is N*N complex, because recalculate the
+centroid when move an item from one cluster to another. 
+This new function can be used for low and high dimensionality problems, increasing 
+performance in both cases::
+
+ >>>solution = cl.HDgetclusters(numclusters,max_iterations)
+
+Other new available optimization inside HDcentroid() function in is the use of mean instead median at centroid calculation.
+median is more accurate but involves more computations when N is huge. 
+The function HDcentroid() is invoked internally by HDgetclusters()
+

cluster/HDdistances.py

@@ -0,0 +1,71 @@
+
+# This file provides functionalities for High dimensionality problems but also for low dimensionality problems
+#   - New Distance computation
+#   - SSE metric computation for assist the computation of the optimal number of clusters
+#
+# Authors:
+# Jose Javier Garcia Aranda
+# Juan Ramos Diaz
+
+
+
+#from cluster import KMeansClustering
+#import KMeansClustering
+#import ClusteringError
+import util
+import time
+import datetime
+
+import random
+
+def HDdistItems(profile1,profile2):
+    #Distance function, this distance between two profiles is based on:
+    #For each keyword of user A, if the keyword is not  present in user B , then the distance for this keyword is the weight in the user A.
+    #If the keyword exists in both users, the weights are compared and the distance is the absolute difference
+    len1=len(profile1)/2
+    len2=len(profile2)/2
+    total_len=len1+len2 #this value usually is 20
+    factor_len=20.0/total_len #this only work if the profile has less than 10 keys
+    distance = 0.0
+    marked=[0]*(total_len*2);
+    for i in range(len1):
+        found=False
+        for j in range(len2):
+            if profile1[i*2]==profile2[j*2]:
+                distance+=abs(profile1[i*2+1]-profile2[j*2+1]);
+                found=True;
+                marked[j*2]=1;
+                break;
+        if found==False:
+            distance+=profile1[i*2+1];
+
+    for i in range(len2):
+        if marked[i*2]==1:
+            continue;
+        distance+=profile2[i*2+1]
+
+    distance=distance*factor_len
+    return distance
+
+def HDequals(profile1,profile2):
+    for i in range(10):
+        for j in range(10):
+            if profile1[i*2]!=profile2[j*2]:
+                return False
+            elif profile1[i*2+1]!=profile2[j*2+1]:
+                return False
+            return True
+    #return True;
+
+def HDcomputeSSE(solution,numclusters):
+    #This metric measure the cohesion of users into a cluster and the separation among clusters at the same time
+    partial_solution=0
+    total_solution=0
+    dist=0
+    for i in range(numclusters):
+        partial_solution=0
+        for j in solution[i]:
+            dist=HDdistItems(util.HDcentroid(solution[i]),j)
+            partial_solution+=dist*dist
+            total_solution+=partial_solution
+    return total_solution

cluster/method/kmeans.py

@@ -14,9 +14,12 @@
 # along with this library; if not, write to the Free Software Foundation,
 # Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
 #
+# new functions: HDgetCluster() and HDassignItem by:
+#   2015 Jose Javier Garcia Aranda, Juan Ramos Diaz
 
-
-from cluster.util import ClusteringError, centroid, minkowski_distance
+from cluster.util import ClusteringError, centroid, minkowski_distance, HDcentroid
+import time
+import datetime
 
 
 class KMeansClustering(object):
@@ -166,3 +169,103 @@ def initialise_clusters(self, input_, clustercount):
         for item in input_:
             self.__clusters[count % clustercount].append(item)
             count += 1
+
+
+    def HDgetclusters(self, count,  max_iterations):
+        """
+        Generates *count* clusters.
+
+        :param count: The amount of clusters that should be generated.  count
+            must be greater than ``1``.
+        :raises ClusteringError: if *count* is out of bounds.
+        """
+
+        # only proceed if we got sensible input
+        if count <= 1:
+            raise ClusteringError("When clustering, you need to ask for at "
+                                  "least two clusters! "
+                                  "You asked for %d" % count)
+
+        # return the data straight away if there is nothing to cluster
+        if (self.__data == [] or len(self.__data) == 1 or
+                count == self.__initial_length):
+            return self.__data
+
+        # It makes no sense to ask for more clusters than data-items available
+        if count > self.__initial_length:
+            raise ClusteringError(
+                "Unable to generate more clusters than "
+                "items available. You supplied %d items, and asked for "
+                "%d clusters." % (self.__initial_length, count))
+
+        self.initialise_clusters(self.__data, count)
+
+        items_moved = True  # tells us if any item moved between the clusters,
+                            # as we initialised the clusters, we assume that
+                            # is the case
+
+        iteration=0
+        #asi no, no obligar a hacer iteraciones, lo hago segun dice el algoritmo
+        #pero si llego a iteraciones paro, si termino antes de llegar, mejor
+        while items_moved is True:
+            items_moved = False
+            print "iterating",iteration
+            ts = time.time()
+            st=datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')
+            print st
+            iteration=iteration+1
+
+            #computation of centroids
+            my_centroids={}   # new!!     
+            for cluster in self.__clusters:# new!! 
+                one_centroid=HDcentroid(cluster)# new!!
+                my_centroids[one_centroid]=cluster # new!!
+
+
+
+
+            #this few lines are new:    
+            #print centroids . it works, for debug purposes only!!
+            #for i in my_centroids.keys():
+            #    print "key:",i # print the centroid!!
+            #    print "value:",my_centroids[i] # print all elements of the cluster!!
+            #print my_centroids.keys()[0] # imprime el primer centroide. es una prueba
+
+            #now we scan the N items without recalculation of centroids. Therefore, it is linear
+            for cluster in self.__clusters:
+                for centroid_aux, cluster_aux in my_centroids.iteritems():
+                    if cluster_aux == cluster:
+                        centroid_cluster=centroid_aux
+                        break;
+                for item in cluster:
+                    res = self.HDassign_item(item, cluster,centroid_cluster,my_centroids)#modified!!
+                    if items_moved is False:
+                        items_moved = res
+
+            if (iteration == max_iterations):
+                items_moved = False          
+        return self.__clusters
+
+
+    def HDassign_item(self, item, origin, origin_centroid, my_centroids):
+        """
+        Assigns an item from a given cluster to the closest located cluster.
+
+        :param item: the item to be moved.
+        :param origin: the originating cluster.
+        :param origin_centroid: centroid of the originating cluster
+        :my_centroids: dictionary of centroid,cluster
+        """
+        closest_cluster=origin #my_centroids[closest_centroid]=closest_cluster
+        closest_centroid=origin_centroid
+        #for cluster in self.__clusters:
+        for centro in my_centroids.keys():
+            if self.distance(item, centro) < self.distance(
+                    item, closest_centroid):
+                closest_cluster = my_centroids[centro]
+
+        if id(closest_cluster) != id(origin):
+            self.move_item(item, origin, closest_cluster)
+            return True
+        else:
+            return False