rdkit.ML.Cluster.Butina module

Implementation of the clustering algorithm published in: Butina JCICS 39 747-750 (1999)

rdkit.ML.Cluster.Butina.ClusterData(data, nPts, distThresh, isDistData=False, distFunc=<function EuclideanDist>, reordering=False)

clusters the data points passed in and returns the list of clusters

Arguments

• data: a list, tuple, or numpy array of items with the input data (see discussion of _isDistData_ argument for the exception)

• nPts: the number of points to be used

• distThresh: elements within this range of each other are considered to be neighbors

• isDistData: set this toggle when the data passed in is a

  distance matrix. The distance matrix should be stored in one of two formats: as an nxn NumPy array, or as a symmetrically stored list or 1D array generated using a similar process to the example below:

  dists = [] for i in range(nPts):

  for j in range(i):

  dists.append( distfunc(i,j) )

• distFunc: a function to calculate distances between points.

  Receives 2 points as arguments, should return a float

• reordering: if this toggle is set, the number of neighbors is updated

  for the unassigned molecules after a new cluster is created such that always the molecule with the largest number of unassigned neighbors is selected as the next cluster center.

Returns

• a tuple of tuples containing information about the clusters:

  ( (cluster1_elem1, cluster1_elem2, …),

  (cluster2_elem1, cluster2_elem2, …), …

  ) The first element for each cluster is its centroid.

rdkit.ML.Cluster.Butina.EuclideanDist(pi, pj)

Calculate the Euclidean distance between two points.

rdkit.ML.Cluster.Butina.compute_distance_matrix(data, n_pts, dist_func)

Compute the distance matrix for the given data.