Wrapped classes in discrete

[dellaert]

This adds python/MATLAB wrappers for the classes in discrete. @ProfFan helped with some of the wrapper magic.

Development was driven by

• test_DiscreteFactorGraph.py
• test_DiscreteBayesNet.py

To make this possible, I had to do some constructor forwarding etc, but the main new functionality is the ability to construct DiscreteConditionals with a simpler constructor that could be wrapped. You can now do

  DiscreteConditional pXE(X, {E}, "95/5 2/98");

which complements the magical sugar that was there before:

  DiscreteConditional pXE(X | E = "95/5 2/98");

I also added a variant of DiscreteConditional::choose that returns a DecisionTreeFactor.

It works. Here is code that builds the Asia network:

asia = DiscreteBayesNet()
asia.add(Asia, P([]), "99/1")
asia.add(Smoking, P([]), "50/50")

asia.add(Tuberculosis, P([Asia]), "99/1 95/5")
asia.add(LungCancer, P([Smoking]), "99/1 90/10")
asia.add(Bronchitis, P([Smoking]), "70/30 40/60")

asia.add(Either, P([Tuberculosis, LungCancer]), "F T T T")

asia.add(XRay, P([Either]), "95/5 2/98")
asia.add(Dyspnea, P([Either, Bronchitis]), "9/1 2/8 3/7 1/9")

print(asia) yields:

DiscreteBayesNet

size: 8
factor 0:  P( 0 )
  Cardinalities: {0:2, }
  Choice(0) 
  0 Leaf 0.99
  1 Leaf 0.01

factor 1:  P( 4 )
  Cardinalities: {4:2, }
  Leaf 0.5

factor 2:  P( 3 | 0 )
  Cardinalities: {0:2, 3:2, }
  Choice(3) 
  0 Choice(0) 
  0 0 Leaf 0.99
  0 1 Leaf 0.95
  1 Choice(0) 
  1 0 Leaf 0.01
  1 1 Leaf 0.05

factor 3:  P( 6 | 4 )
  Cardinalities: {4:2, 6:2, }
  Choice(6) 
  0 Choice(4) 
  0 0 Leaf 0.99
  0 1 Leaf 0.9
  1 Choice(4) 
  1 0 Leaf 0.01
  1 1 Leaf 0.1

factor 4:  P( 7 | 4 )
  Cardinalities: {4:2, 7:2, }
  Choice(7) 
  0 Choice(4) 
  0 0 Leaf 0.7
  0 1 Leaf 0.4
  1 Choice(4) 
  1 0 Leaf 0.3
  1 1 Leaf 0.6

factor 5:  P( 5 | 3 6 )
  Cardinalities: {3:2, 5:2, 6:2, }
  Choice(6) 
  0 Choice(5) 
  0 0 Choice(3) 
  0 0 0 Leaf 1
  0 0 1 Leaf 0
  0 1 Choice(3) 
  0 1 0 Leaf 0
  0 1 1 Leaf 1
  1 Choice(5) 
  1 0 Leaf 0
  1 1 Leaf 1

factor 6:  P( 2 | 5 )
  Cardinalities: {2:2, 5:2, }
  Choice(5) 
  0 Choice(2) 
  0 0 Leaf 0.95
  0 1 Leaf 0.05
  1 Choice(2) 
  1 0 Leaf 0.02
  1 1 Leaf 0.98

factor 7:  P( 1 | 5 7 )
  Cardinalities: {1:2, 5:2, 7:2, }
  Choice(7) 
  0 Choice(5) 
  0 0 Choice(1) 
  0 0 0 Leaf 0.9
  0 0 1 Leaf 0.1
  0 1 Choice(1) 
  0 1 0 Leaf 0.3
  0 1 1 Leaf 0.7
  1 Choice(5) 
  1 0 Choice(1) 
  1 0 0 Leaf 0.2
  1 0 1 Leaf 0.8
  1 1 Choice(1) 
  1 1 0 Leaf 0.1
  1 1 1 Leaf 0.9

[varunagrawal]

LGTM module some comments. I'll pull this branch and investigate what the CI failure is.

[varunagrawal]

The wrapper supports operator overloading. I am pretty sure we added support for the callable operator.

[dellaert]

Example?

[varunagrawal]

The syntax would be double operator()(const gtsam::DiscreteValues& values) const;

I used this in the DecisionTreeFactor and it works as expected. 🙂

In [1]: f = gtsam.DecisionTreeFactor()

In [2]: f
Out[2]: 
DecisionTreeFactor
Potentials:
  Cardinalities: {}
  Leaf 1

In [3]: f()
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-c43e34e6d405> in <module>
----> 1 f()

TypeError: __call__(): incompatible function arguments. The following argument types are supported:
    1. (self: gtsam.gtsam.DiscreteFactor, arg0: gtsam::Assignment<unsigned long>) -> float

Invoked with: DecisionTreeFactor
Potentials:
  Cardinalities: {}
  Leaf 1


In [4]: values = gtsam.DiscreteValues()

In [5]: f(values)
Out[5]: 1.0

In [6]: 

[varunagrawal]

The repetition is because while the underlying C++ code will call the proper super class method, the pybind11 wrapper needs to know that this is a method associated with this class. It is inefficient for the writer (you in this case) but it serves as a great index of what methods are applicable.

[dellaert]

NOOOOO! That worked before. So, the semantics changed... The wrapper should generate all wrapped functions in base classes I think.

[varunagrawal]

Gotcha. Okay then, we'll log this as a feature request.

[varunagrawal]

@dellaert I fixed the CI issue in #968

[varunagrawal]

I also added the GTSAM_EXPORT declarations in #968 so that Windows is happy.