level_set_tree.construct_tree_from_graph¶
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level_set_tree.construct_tree_from_graph(adjacency_list, density, prune_threshold=None, num_levels=None, verbose=False)¶ Construct a level set tree from a similarity graph and a density estimate.
Parameters: adjacency_list : list [list]
Adjacency list of the k-nearest neighbors graph on the data. Each entry contains the indices of the k closest neighbors to the data point at the same row index.
density : list [float]
Estimate of the density function, evaluated at the data points represented by the keys in adjacency_list.
prune_threshold : int, optional
Leaf nodes with fewer than this number of members are recursively merged into larger nodes. If ‘None’ (the default), then no pruning is performed.
num_levels : list int, optional
Number of density levels in the constructed tree. If None (default), num_levels is internally set to be the number of rows in X.
verbose : bool, optional
If True, a progress indicator is printed at every 100th level of tree construction.
Returns: T : levelSetTree
See the LevelSetTree class for attributes and method definitions.
See also
Examples
>>> X = numpy.random.rand(100, 2) >>> knn_graph, radii = debacl.utils.knn_graph(X, k=8) >>> density = debacl.utils.knn_density(radii, n=100, p=2, k=8) >>> tree = debacl.construct_tree_from_graph(knn_graph, density, ... prune_threshold=5) >>> print(tree) +----+-------------+-----------+------------+----------+------+--------+----------+ | id | start_level | end_level | start_mass | end_mass | size | parent | children | +----+-------------+-----------+------------+----------+------+--------+----------+ | 0 | 0.000 | 0.768 | 0.000 | 0.390 | 100 | None | [1, 2] | | 1 | 0.768 | 1.494 | 0.390 | 0.790 | 30 | 0 | [7, 8] | | 2 | 0.768 | 4.812 | 0.390 | 1.000 | 31 | 0 | [] | | 7 | 1.494 | 2.375 | 0.790 | 0.950 | 6 | 1 | [] | | 8 | 1.494 | 2.308 | 0.790 | 0.940 | 5 | 1 | [] | +----+-------------+-----------+------------+----------+------+--------+----------+