readme, licence

.gitignore

@@ -114,3 +114,5 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.#*

LICENCE

@@ -0,0 +1,19 @@
+Copyright 2019 Steven Holtzen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

README.md

@@ -0,0 +1,53 @@
+# About
+
+This repository contains the implementation that performed the experiments for the 
+research paper:
+
+* Holtzen, Steven, Todd Millstein, and Guy Van den Broeck. ["Generating and
+  Sampling Orbits for Lifted Probabilistic
+  Inference."](https://arxiv.org/abs/1903.04672) Uncertainty in Artificial
+  Intelligence (UAI). 2019.
+
+To cite, please use:
+
+```
+@inproceedings{HoltzenUAI19,
+  author    = {Holtzen, Steven and Millstein, Todd and Van den Broeck, Guy},
+  title     = {Generating and Sampling Orbits for Lifted Probabilistic Inference},
+  booktitle = {Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI)},
+  month     = {jul},
+  year      = {2019},
+}
+```
+
+# Disclaimer
+
+This code is provided as-is. It is what was used to run the experiments in the
+paper. It is typical research-ware; there are many improvements that could be
+made, and it is definitely not ready for prime-time (i.e., production). Think of
+it like extra-detailed documentation on how the algorithm described in the paper
+works.
+
+# Installation
+
+While it may look like regular Python, this code must actually be run
+using the [Sage math library](https://www.sagemath.org/library.html). 
+To use this library:
+
+1. Install sage.
+2. Install `bliss` by running `sage -i bliss` (if you want to hack the code to
+   remove this requirement, it is possible to do so, but it will be much slower)
+3. Run `sage setup.py`
+
+# Organization
+
+* `factor.py` contains the most interesting code. It has a simple implementation of 
+  a factor graph and the exact and approximate lifted inference algorithms
+  from the paper.
+* `my_graphs.py` contains the example factor graphs that are generated
+* `experiments.py` contains stubs for running the experiments from the paper
+* `tests.py` contains some standard test cases that illustrate usage.
+
+To execute these files, use `sage` (*not* the regular Python command). I.e.,
+run `sage test.py`. 
+

experiment.py

@@ -14,35 +14,6 @@
 import itertools
 import time
 
-# holes pigeons, m holes
-def mk_pigeonhole_fg(n, m, order=True):
-    w1 = 10000000
-    w2 = 100000
-    (g, (variables, factors)) = gen_pigeonhole_fg(n, m)
-    def potential(state):
-        total = 0.0
-        # to see every pigeon in exactly one hole
-        for p in range(0, n):
-            # check the holes for the pigeons
-            in_hole = False
-            for h in range(0, m):
-                if state[(p, h)]:
-                    if in_hole:
-                        return 0.000000001
-                    else:
-                        in_hole = True
-            if in_hole:
-                total += w1
-
-
-        # check to see no no hole has 2 pigeons
-        for h in range(0, m):
-            for (p1, p2) in findsubsets(range(0, n), 2):
-                if not state[(p1, h)] or not state[(p2, h)]:
-                    total += w2
-
-        return total
-    return FactorGraph(g, variables, factors, potential)
 
 
 ### computes the total variation distance comparing different sampling methods
@@ -101,6 +72,11 @@ def pigeonhole_dtv():
     model.total_variation(burnside, start, 100)
 
 
+def pigeonhole_exact_lifted():
+    model = mk_pigeonhole_fg(2,5)
+    model.partition()
+
+
 if __name__ == "__main__":
     # run your test here if you want
     pigeonhole_dtv()