Merge branch 'add-astar' of https://github.com/APLA-Toolbox/jl-pddl-wrapper into add-astar

Author: guilyx

main.py

@@ -14,13 +14,16 @@ def main():
     args = args_parser.parse_args()
     apla_tbx = AutomatedPlanner(args.domain, args.problem)
     apla_tbx.logger.info("Starting the planning script")
-    apla_tbx.logger.debug("Available heuristics: " + str(apla_tbx.available_heuristics.keys()))
+    apla_tbx.logger.debug(
+        "Available heuristics: " + str(apla_tbx.available_heuristics.keys())
+    )
 
     path, computation_time = apla_tbx.dijktra_best_first_search(time_it=True)
     apla_tbx.logger.debug(apla_tbx.get_actions_from_path(path))
 
     apla_tbx.logger.debug("Computation time: %.2f seconds" % computation_time)
     apla_tbx.logger.info("Terminate with grace...")
 
+
 if __name__ == "__main__":
     main()

src/a_star.py

@@ -28,7 +28,9 @@ def __hash(self, node):
 
     def search(self):
         time_start = now()
-        self.automated_planner.logger.debug("Search started at: " + str(timestamp.now()))
+        self.automated_planner.logger.debug(
+            "Search started at: " + str(timestamp.now())
+        )
         while self.open_nodes_n > 0:
             current_key = min(
                 [n for n in self.nodes if self.nodes[n].is_open],
@@ -40,7 +42,9 @@ def search(self):
                 self.automated_planner.problem.goal, current_node.state
             ):
                 computation_time = now() - time_start
-                self.automated_planner.logger.debug("Search finished at: " + str(timestamp.now()))
+                self.automated_planner.logger.debug(
+                    "Search finished at: " + str(timestamp.now())
+                )
                 return current_node, computation_time
 
             current_node.is_closed = True

src/automated_planner.py

@@ -5,10 +5,14 @@
 from .heuristics import goal_count_heuristic, zero_heuristic
 import coloredlogs, logging
 import julia
+
 _ = julia.Julia(compiled_modules=False, debug=False)
 from julia import PDDL
 from time import time as now
+
 logging.getLogger("julia").setLevel(logging.WARNING)
+
+
 class AutomatedPlanner:
     def __init__(self, domain_path, problem_path, log_level="DEBUG"):
         # Planning Tool

src/bfs.py

@@ -12,7 +12,9 @@ def __init__(self, automated_planner):
 
     def search(self):
         time_start = now()
-        self.automated_planner.logger.debug("Search started at: " + str(timestamp.now()))
+        self.automated_planner.logger.debug(
+            "Search started at: " + str(timestamp.now())
+        )
         while self.queue:
             current_node = self.queue.pop(0)
             if current_node not in self.visited:
@@ -22,7 +24,9 @@ def search(self):
                     self.automated_planner.problem.goal, current_node.state
                 ):
                     computation_time = now() - time_start
-                    self.automated_planner.logger.debug("Search finished at: " + str(timestamp.now()))
+                    self.automated_planner.logger.debug(
+                        "Search finished at: " + str(timestamp.now())
+                    )
                     return current_node, computation_time
 
                 actions = self.automated_planner.available_actions(current_node.state)

src/dfs.py

@@ -2,6 +2,7 @@
 from datetime import datetime as timestamp
 from time import time as now
 
+
 class DepthFirstSearch:
     def __init__(self, automated_planner):
         self.visited = []
@@ -11,7 +12,9 @@ def __init__(self, automated_planner):
 
     def search(self):
         time_start = now()
-        self.automated_planner.logger.debug("Search started at: " + str(timestamp.now()))
+        self.automated_planner.logger.debug(
+            "Search started at: " + str(timestamp.now())
+        )
         while self.stack:
             current_node = self.stack.pop(0)
             if current_node not in self.visited:
@@ -21,7 +24,9 @@ def search(self):
                     self.automated_planner.problem.goal, current_node.state
                 ):
                     computation_time = now() - time_start
-                    self.automated_planner.logger.debug("Search finished at: " + str(timestamp.now()))
+                    self.automated_planner.logger.debug(
+                        "Search finished at: " + str(timestamp.now())
+                    )
                     return current_node, computation_time
 
                 actions = self.automated_planner.available_actions(current_node.state)

src/dijkstra.py

@@ -5,6 +5,7 @@
 from datetime import datetime as timestamp
 from time import time as now
 
+
 class DijkstraBestFirstSearch:
     def __init__(self, automated_planner):
         self.automated_planner = automated_planner
@@ -13,7 +14,7 @@ def __init__(self, automated_planner):
             automated_planner,
             is_closed=False,
             is_open=True,
-            heuristic=zero_heuristic
+            heuristic=zero_heuristic,
         )
         self.open_nodes_n = 1
         self.nodes = dict()
@@ -25,7 +26,9 @@ def __hash(self, node):
         return string.split(sep, 1)[0] + ")"
 
     def search(self):
-        self.automated_planner.logger.debug("Search started at: " + str(timestamp.now()))
+        self.automated_planner.logger.debug(
+            "Search started at: " + str(timestamp.now())
+        )
         time_start = now()
         while self.open_nodes_n > 0:
             current_key = min(
@@ -38,7 +41,9 @@ def search(self):
                 self.automated_planner.problem.goal, current_node.state
             ):
                 computation_time = now() - time_start
-                self.automated_planner.logger.debug("Search finished at: " + str(timestamp.now()))
+                self.automated_planner.logger.debug(
+                    "Search finished at: " + str(timestamp.now())
+                )
                 return current_node, computation_time
 
             current_node.is_closed = True

tests/test_astar.py

@@ -11,13 +11,17 @@
 def test_astar_init():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     astar = AStarBestFirstSearch(apla, apla.available_heuristics["goal_count"])
-    assert astar.init.h_cost == apla.available_heuristics["goal_count"](apla.initial_state, apla)
+    assert astar.init.h_cost == apla.available_heuristics["goal_count"](
+        apla.initial_state, apla
+    )
+
 
 def test_astar_goal():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     path, _ = apla.astar_best_first_search()
     assert apla.available_heuristics["goal_count"](path[-1], apla) == 0
 
+
 def test_astar_path_length():
     apla = AutomatedPlanner("data/domain.pddl", "data/problem.pddl")
     path, _ = apla.astar_best_first_search()