Add call graph breadth first search to solve graphs actions

.github/workflows/python-tests.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: windows-latest
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10']
+        python-version: ['3.10', '3.11', '3.12']
     steps:
     - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}

.pre-commit-config.yaml

@@ -15,17 +15,17 @@ repos:
     hooks:
       - id: pytest-fast-check
         name: pytest-fast-check
-        entry: ./venv/Scripts/python.exe -m pytest -m "not slow"
-        stages: ["commit"]
+        entry: pytest -m "not slow"
+        stages: ["pre-commit"]
         language: system
         pass_filenames: false
         always_run: true
   - repo: local
     hooks:
       - id: pytest-check
         name: pytest-check
-        entry: ./venv/Scripts/python.exe -m pytest
-        stages: ["push"]
+        entry: pytest
+        stages: ["pre-push"]
         language: system
         pass_filenames: false
         always_run: true

README.rst

@@ -90,7 +90,7 @@ Here is an example to show how could we hierarchicaly build an explanable behavi
       def __init__(self, hand) -> None:
          super().__init__(name="Is hand near the cat ?")
          self.hand = hand
-      def __call__(self, observation):
+      def __call__(self, observation) -> int:
          # 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
@@ -119,7 +119,7 @@ Here is an example to show how could we hierarchicaly build an explanable behavi
    class IsThereACatAround(FeatureCondition):
       def __init__(self) -> None:
          super().__init__(name="Is there a cat around ?")
-      def __call__(self, observation):
+      def __call__(self, observation) -> int:
          # 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
@@ -217,7 +217,7 @@ Will generate the code bellow:
    # 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):
+      def __call__(self, observation) -> Any:
          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)

commands/coverage.ps1

@@ -0,0 +1 @@
+pytest --cov=src --cov-report=html --cov-report=term

setup.py

@@ -0,0 +1,4 @@
+from setuptools import setup
+
+
+setup()

src/hebg/__init__.py

@@ -1,5 +1,5 @@
 # HEBGraph for explainable hierarchical reinforcement learning
-# Copyright (C) 2021-2022 Mathïs FEDERICO <https://www.gnu.org/licenses/>
+# Copyright (C) 2021-2024 Mathïs FEDERICO <https://www.gnu.org/licenses/>
 
 """A structure for explainable hierarchical reinforcement learning"""
 

src/hebg/behavior.py

@@ -1,5 +1,5 @@
 # HEBGraph for explainable hierarchical reinforcement learning
-# Copyright (C) 2021-2022 Mathïs FEDERICO <https://www.gnu.org/licenses/>
+# Copyright (C) 2021-2024 Mathïs FEDERICO <https://www.gnu.org/licenses/>
 
 """Module for base Behavior."""
 
@@ -17,11 +17,11 @@
 class Behavior(Node):
     """Abstract class for a Behavior as Node"""
 
-    def __init__(self, name: str, image=None) -> None:
-        super().__init__(name, "behavior", image=image)
+    def __init__(self, name: str, image=None, **kwargs) -> None:
+        super().__init__(name, "behavior", image=image, **kwargs)
         self._graph = None
 
-    def __call__(self, observation, *args, **kwargs):
+    def __call__(self, observation, *args, **kwargs) -> None:
         """Use the behavior to get next actions.
 
         By default, uses the HEBGraph if it can be built.