perturbationx.topo_npa.permutation

Submodules

Package Contents

Functions

permute_adjacency(adj[, permutations, iterations, ...])

Permute an adjacency matrix.

permute_edge_list(edge_list[, node_list, iterations, ...])

Permute an edge list.
perturbationx.topo_npa.permutation.permute_adjacency(adj: numpy.ndarray | scipy.sparse.sparray, permutations=('k2',), iterations=500, permutation_rate=1.0, seed=None)

Permute an adjacency matrix.

Parameters:

  • adj (np.ndarray | sp.sparray) – The adjacency matrix to permute.

  • permutations (list, optional) – The permutations to apply. May contain ‘k1’ and ‘k2’ in any order. Defaults to (‘k2’,).

  • iterations (int, optional) – The number of permutations to generate. Defaults to 500.

  • permutation_rate (float, optional) – The fraction of edges to permute. Defaults to 1.

  • seed (int, optional) – The seed for the random number generator.

Raises:

ValueError – If the adjacency matrix is not square.

Returns:

A dictionary of lists with permuted adjacency matrices, keyed by the permutation name.

Return type:

dict

perturbationx.topo_npa.permutation.permute_edge_list(edge_list: numpy.ndarray, node_list=None, iterations=500, method='k1', permutation_rate=1.0, seed=None)

Permute an edge list.

Parameters:

  • edge_list (np.ndarray) – The edge list to permute. Must be a 2D array with shape (n_edges, 4). The first two columns contain the source and target nodes, the third column contains the edge type, and the fourth column contains the confidence weight. Confidence weights are optional.

  • node_list (list, optional) – The list of nodes to use in the permutation. Only edges that connect nodes in this list are permuted. If None, the list is inferred from the edge list.

  • iterations (int, optional) – The number of permutations to generate. Defaults to 500.

  • method (str, optional) – The permutation method to use. Defaults to ‘k1’. May be ‘k1’ or ‘k2’.

  • permutation_rate (float | str, optional) – The fraction of edges to permute. Defaults to 1. If ‘confidence’, the confidence weights are used to determine the number of edges to permute. For each edge, a random number is drawn from a uniform distribution between 0 and 1. If the confidence weight is larger than this number, the edge is permuted.

  • seed (int, optional) – The seed for the random number generator.

Raises:

ValueError – If the permutation method is unknown.

Returns:

A list of permutations. Each permutation is a list of tuples with the source node, target node, and edge type. If the edge type is None, the edge is removed.