Please use Python version 3.6 or later.
Required packages are listed in requirements.txt and can be installed with pip install -r requirements.txt.
To run pyAGA, please install the SCIP optimization suite version 7.0.3 from https://www.scipopt.org/, see here for licensing details. Make sure SCIP is added to your system's PATH variable.
On your command line, just invoke
python example_calculation.py
to run an example calculation with fixed parameters.
An interactive matplotlib window is opened, containing a histogram of the edge weights and the bins. The calculation starts after you close this window. It calculates the automorphism group for a world with 20x10-pixels and translational invariance. After running for 5-6 minutes, this yields the expected 400 permutations.
If you want to run multiple tests with different parameters, consider running the script calculate_automorphisms.py
instead by running
python calculate_automorphisms.py <testcase>
where <testcase> corresponds to a configuration
file inside the subdirectory calculations/ called <testcase>.ini.
An example configuration file for the calculate_automorphisms-module is given by:
[global_timeout]
# Time until the entire run is terminated
global_timeout = 1000
[main]
# Maximum time any one iteration can process until it is terminated
time_per_iteration = 1000
# The prefix of the matrices used (decide which world/dataset to consider)
world_name = two_letter_words_20x10
error_value_limits = 0.01
percentages = 50.0
kde_bandwidths = 3.3e-4
fault_tolerance_ratios = 0.2, 0.25
This configuration is identical to the configuration in the file calculations/20x10_short_test.ini, therefore to run it you
need to use python calculate_automorphisms.py 20x10_short_test. This will run for approximately 11-12 minutes and yield that
with the fault tolerance of 20%, we only find 20 automorphisms, while a fault tolerance of 25% allows us to find
all 400 expected automorphisms.
The bottom four parameters error_value_limits, percentages, kde_bandwidths, and fault_tolerance_ratios can be
simple floats or tuples of floats, in which case the cartesian product of all values will be calculated and the algorithm will be run
for every element of this cartesian product. The results are then saved to
calculations/results/<testcase>_results_<uuid4_string>.xlsx. The random
uuid4_string is added to prevent accidental overwriting of previously calculated results.
As all matrices are stored in the subdirectory data/, this will load the adjacency matrices
- data/two_letter_words_20x10_concurrence_matrix_50.0.pickle and
- data/two_letter_words_20x10_concurrence_matrix_60.0.pickle
and perform the automorphism calculation.
If you want to try a different world or a different percentage of unique observations, change the parameter world_name and percentages accordingly.
For a detailed explanation into the consequence of these parameters, we refer to the publication mentioned above.
Copyright (c) 2021 Merck KGaA, Darmstadt, Germany
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
The full text of the license can be found in the file LICENSE in the repository root directory.