rdkit.ML.Cluster.Butina

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# Source Code for Module rdkit.ML.Cluster.Butina

      1  # $Id$
      2  #
      3  # Copyright (C) 2007-2008 Greg Landrum
      4  #   All Rights Reserved
      5  #
      6  """ Implementation of the clustering algorithm published in:
      7    Butina JCICS 39 747-750 (1999)
      8
      9  """
     10  import numpy
     11  from rdkit import RDLogger
     12  logger=RDLogger.logger()
     13
     14 -def EuclideanDist(pi,pj):
     15    dv = numpy.array(pi)- numpy.array(pj)
     16    return numpy.sqrt(dv*dv)
     17
     18
     19 -def ClusterData(data,nPts,distThresh,isDistData=False,distFunc=EuclideanDist,reordering=False):
     20    """  clusters the data points passed in and returns the list of clusters
     21
     22      **Arguments**
     23
     24        - data: a list of items with the input data
     25          (see discussion of _isDistData_ argument for the exception)
     26
     27        - nPts: the number of points to be used
     28
     29        - distThresh: elements within this range of each other are considered
     30          to be neighbors
     31
     32        - isDistData: set this toggle when the data passed in is a
     33            distance matrix.  The distance matrix should be stored
     34            symmetrically. An example of how to do this:
     35
     36              dists = []
     37              for i in range(nPts):
     38                for j in range(i):
     39                  dists.append( distfunc(i,j) )
     40
     41        - distFunc: a function to calculate distances between points.
     42             Receives 2 points as arguments, should return a float
     43
     44        - reodering: if this toggle is set, the number of neighbors is updated
     45             for the unassigned molecules after a new cluster is created such
     46             that always the molecule with the largest number of unassigned
     47             neighbors is selected as the next cluster center.
     48
     49      **Returns**
     50
     51        - a tuple of tuples containing information about the clusters:
     52           ( (cluster1_elem1, cluster1_elem2, ...),
     53             (cluster2_elem1, cluster2_elem2, ...),
     54             ...
     55           )
     56           The first element for each cluster is its centroid.
     57
     58    """
     59    if isDistData and len(data)>(nPts*(nPts-1)/2):
     60      logger.warning("Distance matrix is too long")
     61    nbrLists = [None]*nPts
     62    for i in range(nPts): nbrLists[i] = []
     63
     64    dmIdx=0
     65    for i in range(nPts):
     66      for j in range(i):
     67        if not isDistData:
     68          dij = distFunc(data[i],data[j])
     69        else:
     70          dij = data[dmIdx]
     71          dmIdx+=1
     72        if dij<=distThresh:
     73          nbrLists[i].append(j)
     74          nbrLists[j].append(i)
     75    #print nbrLists
     76    # sort by the number of neighbors:
     77    tLists = [(len(y),x) for x,y in enumerate(nbrLists)]
     78    tLists.sort(reverse=True)
     79
     80    res = []
     81    seen = [0]*nPts
     82    while tLists:
     83      nNbrs,idx = tLists.pop(0)
     84      if seen[idx]:
     85        continue
     86      tRes = [idx]
     87      for nbr in nbrLists[idx]:
     88        if not seen[nbr]:
     89          tRes.append(nbr)
     90          seen[nbr]=1
     91      # update the number of neighbors:
     92      # remove all members of the new cluster from the list of
     93      # neighbors and reorder the tLists
     94      if reordering:
     95        # get the list of affected molecules, i.e. all molecules
     96        # which have at least one of the members of the new cluster
     97        # as a neighbor
     98        nbrNbr = [nbrLists[t] for t in tRes]
     99        nbrNbr = frozenset().union(*nbrNbr)
    100        # loop over all remaining molecules in tLists but only
    101        # consider unassigned and affected compounds
    102        for x,y in enumerate(tLists):
    103          y1 = y[1]
    104          if seen[y1] or (y1 not in nbrNbr): continue
    105          # update the number of neighbors
    106          nbrLists[y1] = set(nbrLists[y1]).difference(tRes)
    107          tLists[x] = (len(nbrLists[y1]), y1)
    108        # now reorder the list
    109        tLists.sort(reverse=True)
    110      res.append(tuple(tRes))
    111    return tuple(res)
    112