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v1.1, January 2016

Features

  • The method branch_partition has been added to the LevelSetTree. This method assigns each point a label corresponding to the highest density node to which the point belongs in the level set tree.

  • The tree table now prints when the tree is called by itself. Now both print(tree) and tree print the tree's summary table to the console.

Bugfixes

  • Tree node colors should now match between the figure and the color_nodes output of the LevelSetTree plot method.

  • The knn_density function in the utils module has more informative error messages and warnings for numerical issues, typically resulting from high-dimensional data.

v1.0, November 2015

This release is a major overhaul of DeBaCl. The primary goal is to make the level set trees (LSTs) easier to use, by removing much of the experimental and quasi-analysis code from my dissertation work, adding unit tests to improve code robustness, and simplifying the level set tree API. The experimental code will not vanish; I will move it to separate branches or a new repository. - Brian (papayawarrior)

Logistics

  • No more dependency on igraph. Graph computation is now done with Networkx.

  • No more dependency on Pandas. Level set tree printing is now done with Prettytable.

  • The dependencies on Scipy and Matplotlib are now recommended but optional. Scipy is now used only for constructing similarity graphs by brute force.

  • Saving and loading now use cPickle instead of scipy.io’s loadmat function.

  • The level set tree constructor functions and the LevelSetTree are now accessible directly from the debacl namespace.

Level set tree construction

  • The main level set tree class GeomTree has been renamed to LevelSetTree.

  • The similarity graph LST constructor constructTree has been renamed to construct_tree_from_graph.

  • construct_tree_from_graph now takes the similarity graph in the form of an adjacency list, rather than an adjacency matrix.

  • construct_tree_from_graph no longer requires the user to pre-compute density levels and "background sets" of instances. The function now requires only an adjacency list (to represent a similarity graph) and a density estimate for each data instance.

  • LevelSetTree objects contain the density estimate for each input instance, rather than a collection of background sets.

  • The similarity graph utilities knn_graph and epsilon_graph now return adjacency lists rather than adjacency matrices.

  • The constructDensityGrid utility has been split into to two functions: define_density_mass_grid and define_density_level_grid. The LST constructors use the mass option, but the density level option is left for legacy purposes.

  • The gaussianGraph utility has been removed.

Level set tree printing and plotting

  • Changed tree table column names from 'lambda1', 'lambda2', 'alpha1', and 'alpha2' to 'start_level', 'end_level', 'start_mass', 'end_mass'.

  • The level set tree plot forms have been renamed from 'lambda', 'alpha', and 'kappa' to 'density', 'mass', and 'branch-mass'.

  • The 'width' parameter in the LevelSetTree.plot method has been renamed to 'horizontal_spacing', and the 'mass' option for this parameter has been renamed to 'proportional'.

  • Added a tree method ‘get_leaf_nodes’ which just returns the indices of the leaf nodes.

  • Tree plotting now returns the color assigned to each node.

  • Tree plotting no longer returns the ‘segmap’ and ‘splitmap’ objects.

  • Tree plot objects ‘segments’ and ‘splits’ have been renamed to ‘node_coords’ and ‘split_coords’.

  • The interactive plotting tools ComponentGUI and ClusterGUI have been removed.

  • Plotting utilities (Palette, plot_foreground, make_color_matrix, and setPlotParams) have been removed.

  • The clusterHistogram utility for illustrating the level set tree method on 1D data has been removed.

  • The plot method of LevelSetTree objects no longer accept the 'gap' parameter for adding extra whitespace on the bottom of the plot.

  • The 'old' form of level set tree plots has been removed.

  • The plot method of LevelSetTree objects no longer accept the 'sort' parameter; the branches are always sorted now from highest to lowest mass.

Level set tree pruning

  • Level set tree pruning can now be done directly in the tree constructors. There’s no need to call the prune method separately (although it's still a valid pattern).

  • The prune method returns a new, pruned LevelSetTree object. This means pruning at various thresholds can be done from the same level set tree, without re-building the tree each time.

  • The prune no longer takes a method parameter. It assumes the 'merge-by-size' method.

  • LevelSetTree objects now have a prune_threshold attribute.

Level set tree clustering

  • Changed the name of get_cluster_labels to get_clusters.

  • Changed the name 'all-mode' clustering to 'leaf' clustering.

  • Added the ‘fill_backround’ flag to get_clusters to fill the background points with -1.

  • Changed all clustering methods to return only cluster labels, not the list of active nodes.

  • An instance's cluster label is now the index of the level set tree node that is "activated" by a given clustering method and to which the instance belongs. Previously cluster labels were consecutive integers.

  • Added a utility function reindex_cluster_labels to re-index cluster labels to be consecutive integers.

  • The assignBackgroundPoints utility function for assigning low-density points to clusters has bee removed. Any classifier (in scikit-learn, for example) can be used for this task.

Bugfixes

  • External library imports are now hidden to avoid namespace pollution.

  • The num_levels attribute is now correctly populated.

Miscellaneous

  • Use Python built-in logging module instead of print statements.

  • The subgraphs attribute of a LevelSetTree is now hidden from the user.

  • Helper LevelSetTree methods are now hidden from the user.

  • The cd_tree.py module containing the original level set tree algorithm (Chaudhuri & Dasgupta, 2010) tree has been removed.

  • The drawSample utility has been removed. This can be done now with Numpy.

v0.2, July 2013

Initial release