pldag 0.8.2

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Primitive Logic Directed Acyclic Graph

"Primitive Logic Directed Acyclic Graph" data structure, or "PL-DAG" for short, is fundamentally a Directed Acyclic Graph (DAG) where each node represents a logical relationship, and the leaf nodes correspond to literals. Each node in the graph encapsulates information about how its incoming nodes or leafs are logically related. For instance, a node might represent an AND operation, meaning that if it evaluates to true, all its incoming nodes or leafs must also evaluate to true.

How it works

Each composite (node) is a linear inequality equation on the form A = a + b + c >= 0. A primitive (leaf) is just a name or alias connected to a literal. A literal here is a complex number of two values -1+3j indicating what's the lowest value some variable could take (-1) and the highest value (+3). So a boolean primitive would have the literal value 1j, since it can take on the value 0 or 1. Another primitive having 52j (weeks for instance) could potentially take on every discrete value in between but is expressed only with the lowest and highest value.

Example

from pldag import PLDAG

# Init model
model = PLDAG()

# Sets x, y and z as boolean variables in model
model.set_primitives("xyz")

# Create a simple AND connected to "A"
# This is equivalent to A = x + y + z -3 >= 0
# The ID for this proposition is returned. We can also connect an alias to it, like so.
id_ref = model.set_and(["x","y","z"], alias="A")

# Later if we forget the ID, we can retrieve it like this
id_ref_again = model.id_from_alias("A")
assert id_ref == id_ref_again

# So if we check when all x, y and z are set to 1, then we
# expect `id_ref` to be 1+1j
assert model.propagate({"x": 1+1j, "y": 1+1j, "z": 1+1j}).get(id_ref) == 1+1j

# And then not all are set, we'll get just 1j (meaning the model doesn't now whether it's true or false)
assert model.propagate({"x": 1+1j, "y": 1+1j, "z": 1j}).get(id_ref) == 1j

# However, if we now that any variable is not set, being equal to 0, then the model know the composite to be false (or 0j)
assert model.propagate({"x": 1+1j, "y": 1+1j, "z": 0j}).get(id_ref) == 0j

There's also a quick way to use a solver. There's no built-in solver but is dependent on existing once. Before using, reinstall the package with the solver variable set to the solver you'd want to use

pip install pldag[glpk]

And then you can use it like following

from pldag import Solver

# Maximize [x=1, y=0, z=0] such that rules in model holds and variable `id_ref` must be true.
solution = next(iter(model.solve(objectives=[{"x": 1}], assume={id_ref: 1+1j}, solver=Solver.GLPK)))

# Since x=1 and `id_ref` must be set (i.e. all(x,y,z) must be true), we could expect all variables
# be set.
assert solution.get(id_ref) == 1+1j