Another one

Author: khozzy

Experiments.md

@@ -1,7 +1,7 @@
 # Docker Experiments
 
-Build Docker image with current ACS implementation and Jupyter Lab image    
-    
+Build Docker image with current ACS implementation and Jupyter Lab image   
+
     docker build -f Dockerfile.experiments -t acs . 
 
 You can run it locally to test if everything is working fine
@@ -23,5 +23,7 @@ To run Jupyter notebook to see processed results type:
 To run a single parametrized experiment type something like:
 
     docker run -d --rm -v `pwd`/notebooks:/code/notebooks --name mountaincar khozzy/acs-notebook papermill "notebooks/ACS2_in_MountainCar.ipynb" "notebooks/mc_500k_DecayFalse_10bins.ipynb" --log-output -p trials 500000 -p decay False -p bins 10
+    
+    docker run --rm -v `pwd`/notebooks:/code/notebooks --name "MC" acs:3 papermill "notebooks/MountainCar.ipynb" "notebooks/results/mc.ipynb" --log-output -p trials 1000 -p decay False -p bins 14 -p gamma 0.97 -p biased_exploration 0
 
 

Makefile

@@ -1,4 +1,5 @@
 .PHONY: docs
+experiments_image = acs:2
 
 docs:
 	(cd docs && make html)
@@ -13,6 +14,11 @@ test: lint
 notebook:
 	jupyter lab --notebook-dir notebooks/
 
+publish_experiments_docker_image:
+	docker build -f Dockerfile.experiments -t $(experiments_image) .
+	docker tag $(experiments_image) khozzy/$(experiments_image)
+	docker push khozzy/$(experiments_image)
+
 execute_notebooks:
 #	papermill notebooks/rACS_Corridor.ipynb docs/source/notebooks/rACS_Corridor.ipynb
 	papermill notebooks/FrozenLake.ipynb docs/source/notebooks/FrozenLake.ipynb

lcs/agents/acs2/Configuration.py

@@ -96,26 +96,5 @@ def __init__(self,
         self.mu = mu
         self.chi = chi
 
-    def __str__(self):
-        return "ACS2Configuration:" \
-               "\n\t- Classifier length: [{}]" \
-               "\n\t- Number of possible actions: [{}]" \
-               "\n\t- Environment adapter function: [{}]" \
-               "\n\t- Fitness function: [{}]" \
-               "\n\t- Do GA: [{}]" \
-               "\n\t- Do subsumption: [{}]" \
-               "\n\t- Do Action Planning: [{}]" \
-               "\n\t- Beta: [{}]" \
-               "\n\t- ..." \
-               "\n\t- Epsilon: [{}]" \
-               "\n\t- U_max: [{}]" \
-            .format(self.classifier_length,
-                    self.number_of_possible_actions,
-                    self.environment_adapter,
-                    self.fitness_fcn,
-                    self.do_ga,
-                    self.do_subsumption,
-                    self.do_action_planning,
-                    self.beta,
-                    self.epsilon,
-                    self.u_max)
+    def __str__(self) -> str:
+        return str(vars(self))