HEBG - Hierachical Explainable Behaviors using Graphs

This package is meant to build programatic hierarchical behaviors as graphs to compare them to human explanations of behavior.

We take the definition of "behavior" as a function from observation to action.

Installation

pip install hebg

Usage

Build a HEBGraph

Here is an example to show how could we hierarchicaly build an explanable behavior to pet a cat.

"""

Here is the hierarchical structure that we would want:

```
PetACat:
   IsThereACatAround ?
   -> Yes:
      PetNearbyCat
   -> No:
      LookForACat

PetNearbyCat:
   IsYourHandNearTheCat ?
   -> Yes:
      Pet
   -> No:
      MoveYourHandNearTheCat
```

"""

from hebg import HEBGraph, Action, FeatureCondition, Behavior
from hebg.unrolling import unroll_graph

# Add a fundamental action
class Pet(Action):
   def __init__(self) -> None:
      super().__init__(action="Pet")

# Add a condition on the observation
class IsYourHandNearTheCat(FeatureCondition):
   def __init__(self, hand) -> None:
      super().__init__(name="Is hand near the cat ?")
      self.hand = hand
   def __call__(self, observation):
      # Could be a very complex function that returns 1 is the hand is near the cat else 0.
      if observation["cat"] == observation[self.hand]:
            return int(True)  # 1
      return int(False)  # 0

# Add an unexplainable Behavior (without a graph, but a function that can be called).
class MoveYourHandNearTheCat(Behavior):
   def __init__(self) -> None:
      super().__init__(name="Move slowly your hand near the cat")
   def __call__(self, observation, *args, **kwargs) -> Action:
      # Could be a very complex function that returns actions from any given observation
      return Action("Move hand to cat")

# Add a sub-behavior
class PetNearbyCat(Behavior):
   def __init__(self) -> None:
      super().__init__(name="Pet nearby cat")
   def build_graph(self) -> HEBGraph:
      graph = HEBGraph(self)
      is_hand_near_cat = IsYourHandNearTheCat(hand="hand")
      graph.add_edge(is_hand_near_cat, MoveYourHandNearTheCat(), index=int(False))
      graph.add_edge(is_hand_near_cat, Pet(), index=int(True))
      return graph

# Add an other condition on observation
class IsThereACatAround(FeatureCondition):
   def __init__(self) -> None:
      super().__init__(name="Is there a cat around ?")
   def __call__(self, observation):
      # Could be a very complex function that returns 1 is there is a cat around else 0.
      if "cat" in observation:
            return int(True)  # 1
      return int(False)  # 0

# Add an other unexplainable Behavior (without a graph, but a function that can be called).
class LookForACat(Behavior):
   def __init__(self) -> None:
      super().__init__(name="Look for a nearby cat")
   def __call__(self, observation, *args, **kwargs) -> Action:
      # Could be a very complex function that returns actions from any given observation
      return Action("Move to a cat")

# Finally, add the main Behavior
class PetACat(Behavior):
   def __init__(self) -> None:
      super().__init__(name="Pet a cat")
   def build_graph(self) -> HEBGraph:
      graph = HEBGraph(self)
      is_a_cat_around = IsThereACatAround()
      graph.add_edge(is_a_cat_around, LookForACat(), index=int(False))
      graph.add_edge(is_a_cat_around, PetNearbyCat(), index=int(True))
      return graph

if __name__ == "__main__":
   pet_a_cat_behavior = PetACat()
   observation = {
      "cat": "sofa",
      "hand": "computer",
   }

   # Call on observation
   action = pet_a_cat_behavior(observation)
   print(action)  # Action("Move hand to cat")

   # Obtain networkx graph
   graph = pet_a_cat_behavior.graph
   print(list(graph.edges(data="index")))

   # Draw graph using matplotlib
   import matplotlib.pyplot as plt
   fig, ax = plt.subplots()
   graph.draw(ax)
   plt.show()

Unrolling HEBGraph

When ploting an HEBGraph of a behavior, only the graph of the behavior itself is shown. To see the full hierarchical graph (including sub-behaviors), we need to unroll the graph as such:

from hebg.unrolling import unroll_graph

unrolled_graph = unroll_graph(pet_a_cat_behavior.graph, add_prefix=False)

# Is also a networkx graph
print(list(unrolled_graph.edges(data="index")))

# Draw graph using matplotlib
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
unrolled_graph.draw(ax)
plt.show()

Note that unexplainable behaviors (the one without graphs) are kept as is.

Python code generation from graph

Once you have a HEBGraph, you can use it to generate a working python code that replicates the HEBGraph's behavior:

code = pet_a_cat_behavior.graph.generate_source_code()
with open("pet_a_cat.py", "w") as pyfile:
   pyfile.write(code)

Will generate the code bellow:

from hebg.codegen import GeneratedBehavior

# Require 'Look for a nearby cat' behavior to be given.
# Require 'Move slowly your hand near the cat' behavior to be given.
class PetTheCat(GeneratedBehavior):
   def __call__(self, observation):
      edge_index = self.feature_conditions['Is there a cat around ?'](observation)
      if edge_index == 0:
            return self.known_behaviors['Look for a nearby cat'](observation)
      if edge_index == 1:
            edge_index_1 = self.feature_conditions['Is hand near the cat ?'](observation)
            if edge_index_1 == 0:
               return self.known_behaviors['Move slowly your hand near the cat'](observation)
            if edge_index_1 == 1:
               return self.actions['Action(Pet)'](observation)

Contributing to HEBG

Whenever you encounter a 🐛 bug or have 🎉 feature request, report this via Github issues.

If you wish to contribute directly, see CONTRIBUTING

IRLL / HEB_graphs