Source Code for Module rdkit.ML.Cluster.Murtagh

  1  # $Id: Murtagh.py 997 2009-02-25 06:12:43Z glandrum $ 
  2  # 
  3  # Copyright (C) 2001-2008  greg Landrum and Rational Discovery LLC 
  4  # 
  5  #   @@ All Rights Reserved  @@ 
  6  # 
  7  """ Interface to the C++ Murtagh hierarchic clustering code 
  8   
  9  """ 
 10  from rdkit.ML.Cluster import Clusters 
 11  from rdkit.ML.Cluster.Clustering import MurtaghCluster,MurtaghDistCluster 
 12  import numpy 
 13   
 14   
 15  # constants to select the clustering algorithm 
 16  WARDS=1 
 17  SLINK=2 
 18  CLINK=3 
 19  UPGMA=4 
 20  MCQUITTY=5 
 21  GOWER=6 
 22  CENTROID=7 
 23   
 24  # descriptions of the methods: 
 25  methods = [ 
 26    ("Ward's Minimum Variance",WARDS,"Ward's Minimum Variance"), 
 27    ('Average Linkage',UPGMA,'Group Average Linkage (UPGMA)'), 
 28    ('Single Linkage',SLINK,'Single Linkage (SLINK)'), 
 29    ('Complete Linkage',CLINK,'Complete Linkage (CLINK)'), 
 30  #  ("McQuitty",MCQUITTY,"McQuitty's method"), 
 31  #  ("Gower",GOWER,"Gower's median method"), 
 32    ("Centroid",CENTROID,"Centroid method"), 
 33    ] 
 34   
 35 -def _LookupDist(dists,i,j,n): 
 36    """ *Internal Use Only* 
 37   
 38     returns the distance between points i and j in the symmetric 
 39     distance matrix _dists_ 
 40   
 41    """ 
 42    if i==j: return 0.0 
 43    if i > j: i,j=j,i 
 44    return dists[j*(j-1)/2+i] 
 45   
 46     
 47     
 48 -def _ToClusters(data,nPts,ia,ib,crit,isDistData=0): 
 49    """ *Internal Use Only* 
 50   
 51      Converts the results of the Murtagh clustering code into 
 52      a cluster tree, which is returned in a single-entry list 
 53   
 54    """ 
 55    cs = [None]*nPts 
 56    for i in range(nPts): 
 57      cs[i] = Clusters.Cluster(metric=0.0,data=i,index=(i+1)) 
 58   
 59    nClus = len(ia)-1 
 60    for i in range(nClus): 
 61      idx1 = ia[i]-1 
 62      idx2 = ib[i]-1 
 63      c1 = cs[idx1] 
 64      c2 = cs[idx2] 
 65      newClust = Clusters.Cluster(metric=crit[i],children=[c1,c2], 
 66                                  index=nPts+i+1) 
 67      cs[idx1] = newClust 
 68       
 69    return [newClust] 
 70     
 71   
 72 -def ClusterData(data,nPts,method,isDistData=0): 
 73    """  clusters the data points passed in and returns the cluster tree 
 74   
 75      **Arguments** 
 76   
 77        - data: a list of lists (or array, or whatever) with the input 
 78          data (see discussion of _isDistData_ argument for the exception) 
 79   
 80        - nPts: the number of points to be used 
 81   
 82        - method: determines which clustering algorithm should be used. 
 83            The defined constants for these are: 
 84            'WARDS, SLINK, CLINK, UPGMA' 
 85   
 86        - isDistData: set this toggle when the data passed in is a 
 87            distance matrix.  The distance matrix should be stored 
 88            symmetrically so that _LookupDist (above) can retrieve 
 89            the results: 
 90              for i<j: d_ij = dists[j*(j-1)/2 + i] 
 91   
 92   
 93      **Returns** 
 94   
 95        - a single entry list with the cluster tree 
 96    """ 
 97    data = numpy.array(data) 
 98    if not isDistData: 
 99      sz = data.shape[1] 
100      ia,ib,crit = MurtaghCluster(data,nPts,sz,method) 
101    else: 
102      ia,ib,crit = MurtaghDistCluster(data,nPts,method) 
103    c = _ToClusters(data,nPts,ia,ib,crit,isDistData=isDistData) 
104     
105    return c 
106