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APTED algorithm for the Tree Edit Distance

Project description

Information

This is a Python implementation of the APTED algorithm, the state-of-the-art solution for computing the tree edit distance [1,2], which supersedes the RTED algorithm [3].

It is a port of the original Java implementation available at https://github.com/DatabaseGroup/apted. During the port, some changes were made to reduce the duplication on symmetric operations and to make it look more Pythonic.

You can find more information about APTED on the following website http://tree-edit-distance.dbresearch.uni-salzburg.at/

Citing APTED

If you want to refer to APTED in a publication, please cite [1] and [2].

Licence

The source code is published under the MIT licence found in the root directory of the project and in the header of each source file.

Input

Currently, we support only the so-called bracket notation for the input trees, for example, encoding {A{B{X}{Y}{F}}{C}} corresponds to the following tree:

    A
   / \
  B   C
 /|\
X Y F

Output

Our tool computes two outputs: - tree edit distance value - the minimum cost of transforming the source tree into the destination tree. - tree edit mapping - a mapping between nodes that corresponds to the tree edit distance value. Nodes that are not mapped are deleted (source tree) or inserted (destination tree).

Getting started

This version were tested on Python 2.7, 3.4, 3.5, and 3.6.

First, install it with pip:

pip install apted

If you want to compare the trees {a{b}{c}} and {a{b{d}}}, please run:

python -m apted -t {a{b}{c}} {a{b{d}}} -mv

The output is:

distance:             2
runtime:              0.000270843505859
{a{b}{c}} -> {a{b{d}}}
{c} -> None
{b} -> {b{d}}
None -> {d}

For more information on running options, please run

python -m apted -h

Customizing

It is possible to customize the algorithm to run with custom trees with labels different from simple strings or custom data-structures. Additionally it is possible to customize it to use a more sophisticated cost model than unit cost.

For customizing the algorithm, you can create a custom Config class:

from apted import APTED, Config

class CustomConfig(Config):
   def rename(self, node1, node2):
        """Compares attribute .value of trees"""
        return 1 if node1.value != node2.value else 0

    def children(self, node):
        """Get left and right children of binary tree"""
        return [x for x in (node.left, node.right) if x]

apted = APTED(tree1, tree2, CustomConfig())
ted = apted.compute_edit_distance()
mapping = apted.compute_edit_mapping()

By default, the included Config class consider trees with the atribute name as label and the atribute children as children in left to right preorder.

In addition to the Config class, we also provide a PerEditOperationConfig class that allows you to specify weights for each operation:

from apted import APTED, PerEditOperationConfig

apted = APTED(tree1, tree2, PerEditOperationConfig(.4, .4, .6))
ted = apted.compute_edit_distance()
mapping = apted.compute_edit_mapping()

If your main usage for APTED is to obtain the mapping, it is possible to configure the algorith to keep track of the mapping during the execution. To do so, we provide a function, meta_chained_config, that modifies existing Config classes:

from apted import APTED, PerEditOperationConfig, meta_chained_config

new_config = meta_chained_config(PerEditOperationConfig)
apted = APTED(tree1, tree2, new_config(.4, .4, .6))
ted = apted.compute_edit_distance()
mapping = apted.compute_edit_mapping()

Note that this approach uses much more memory and we didn’t evaluate if it is faster than the original algorithm for the mapping with huge trees. The execution time for the mapping tests were about the same as the original algorithm.

Contributing

Feel free to submit pull resquests to this repository.

The codebase follows the PEP8 conventions. However it is not too strict. For instance, it is okay to have lines with a little more than 79 characters, but try not to exceed too much.

Please, run python test.py during your changes to make sure everything is working. It is also desirable to use coverage.py to check test coverage: coverage run test.py.

Original Authors

  • Mateusz Pawlik
  • Nikolaus Augsten

Implementation Author

  • Joao Felipe Pimentel

References

  1. M. Pawlik and N. Augsten. Tree edit distance: Robust and memory- efficient. Information Systems 56. 2016.
  2. M. Pawlik and N. Augsten. Efficient Computation of the Tree Edit Distance. ACM Transactions on Database Systems (TODS) 40(1). 2015.
  3. M. Pawlik and N. Augsten. RTED: A Robust Algorithm for the Tree Edit Distance. PVLDB 5(4). 2011.

Project details


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