Source code for graspy.utils.ptr

# Copyright 2019 NeuroData (http://neurodata.io)
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import numpy as np
from .utils import import_graph, is_unweighted, is_symmetric, is_loopless, symmetrize
from scipy.stats import rankdata


[docs]def pass_to_ranks(graph, method="simple-nonzero"): r""" Rescales edge weights of an adjacency matrix based on their relative rank in the graph. Parameters ---------- graph: Adjacency matrix method: {'simple-nonzero' (default), 'simple-all', 'zero-boost'} string, optional - 'simple-nonzero' assigns ranks to all non-zero edges, settling ties using the average. Ranks are then scaled by :math:`\frac{rank(\text{non-zero edges})}{\text{total non-zero edges} + 1}` - 'simple-all' assigns ranks to all non-zero edges, settling ties using the average. Ranks are then scaled by :math:`\frac{rank(\text{non-zero edges})}{n^2 + 1}` where n is the number of nodes - 'zero-boost' preserves the edge weight for all 0s, but ranks the other edges as if the ranks of all 0 edges has been assigned. If there are 10 0-valued edges, the lowest non-zero edge gets weight 11 / (number of possible edges). Ties settled by the average of the weight that those edges would have received. Number of possible edges is determined by the type of graph (loopless or looped, directed or undirected). See also -------- scipy.stats.rankdata Returns ------- graph: numpy.ndarray, shape(n_vertices, n_vertices) Adjacency matrix of graph after being passed to ranks """ graph = import_graph(graph) # just for typechecking if is_unweighted(graph): return graph if graph.min() < 0: raise UserWarning( "Current pass-to-ranks on graphs with negative" + " weights will yield nonsensical results, especially for zero-boost" ) if method == "zero-boost": if is_symmetric(graph): # start by working with half of the graph, since symmetric triu = np.triu(graph) non_zeros = triu[triu != 0] else: non_zeros = graph[graph != 0] rank = rankdata(non_zeros) if is_symmetric(graph): if is_loopless(graph): num_zeros = (len(graph[graph == 0]) - graph.shape[0]) / 2 possible_edges = graph.shape[0] * (graph.shape[0] - 1) / 2 else: num_zeros = ( len(triu[triu == 0]) - graph.shape[0] * (graph.shape[0] - 1) / 2 ) possible_edges = graph.shape[0] * (graph.shape[0] + 1) / 2 else: if is_loopless(graph): # n^2 - num_nonzero - num_diagonal num_zeros = graph.size - len(non_zeros) - graph.shape[0] # n^2 - num_diagonal possible_edges = graph.size - graph.shape[0] else: num_zeros = graph.size - len(non_zeros) possible_edges = graph.size # shift up by the number of zeros rank = rank + num_zeros # normalize by the number of possible edges for this kind of graph rank = rank / possible_edges # put back into matrix form (and reflect over the diagonal if necessary) if is_symmetric(graph): triu[triu != 0] = rank graph = symmetrize(triu, method="triu") else: graph[graph != 0] = rank return graph elif method in ["simple-all", "simple-nonzero"]: non_zeros = graph[graph != 0] rank = rankdata(non_zeros) if method == "simple-all": normalizer = graph.size elif method == "simple-nonzero": normalizer = rank.shape[0] rank = rank / (normalizer + 1) graph[graph != 0] = rank return graph else: raise ValueError("Unsuported pass-to-ranks method")