increase coverage and add tests

Author: guilyx

jupyddl/automated_planner.py

@@ -67,7 +67,10 @@ def available_actions(self, state):
         try:
             return self.pddl.available(state, self.domain)
         except (RuntimeError, TypeError, NameError):
-            self.logger.warning("Runtime, Type or Name error occured when fetching available action from state" + str(state))
+            self.logger.warning(
+                "Runtime, Type or Name error occured when fetching available action from state"
+                + str(state)
+            )
             return []
 
     def satisfies(self, asserted_state, state):

jupyddl/data_analyst.py

@@ -367,10 +367,7 @@ def comparative_astar_heuristic_plot(
                 times_y.append(data[node_opened])
 
             ax.plot(
-                nodes_sorted,
-                times_y,
-                "-o",
-                label=h,
+                nodes_sorted, times_y, "-o", label=h,
             )
 
         plt.title("A* heuristics complexity comparison")
@@ -442,10 +439,7 @@ def comparative_data_plot(
             for node_opened in nodes_sorted:
                 times_y.append(data[node_opened])
             ax.plot(
-                nodes_sorted,
-                times_y,
-                "-o",
-                label=planner,
+                nodes_sorted, times_y, "-o", label=planner,
             )
         plt.title("Planners complexity comparison")
         plt.legend(loc="upper left")

jupyddl/heuristics.py

@@ -78,10 +78,8 @@ def compute(self, state):
                 return self.heuristic_keys[self.current_h](costs)
 
             for ax in self.cache.axioms:
-                fact_costs = (
-                    self.automated_planner.pddl.compute_costs_one_step_derivation(
-                        facts, fact_costs, ax, self.current_h
-                    )
+                fact_costs = self.automated_planner.pddl.compute_costs_one_step_derivation(
+                    facts, fact_costs, ax, self.current_h
                 )
 
             actions = self.automated_planner.available_actions(state)
@@ -90,7 +88,9 @@ def compute(self, state):
                     fact_costs, act, domain, self.cache.additions, self.current_h
                 )
 
-            if len(fact_costs) == self.automated_planner.pddl.length(facts) and self.__facts_eq(fact_costs, facts):
+            if len(fact_costs) == self.automated_planner.pddl.length(
+                facts
+            ) and self.__facts_eq(fact_costs, facts):
                 break
 
         return float("inf")

tests/test_basic_astar.py

@@ -35,13 +35,15 @@ def test_astar_path_length():
     path, _, _ = apla.astar_best_first_search()
     assert len(path) > 0
 
+
 def test_astar_path_no_path():
     apla = AutomatedPlanner(
         "pddl-examples/vehicle/domain.pddl", "pddl-examples/vehicle/problem.pddl"
     )
     path, _, _ = apla.astar_best_first_search()
     assert len(path) == 0
 
+
 def test_astar_path_no_heuristic():
     apla = AutomatedPlanner(
         "pddl-examples/flip/domain.pddl", "pddl-examples/flip/problem.pddl"

tests/test_basic_search.py

@@ -1,5 +1,5 @@
 from jupyddl.automated_planner import AutomatedPlanner
-from jupyddl.dijkstra import DijkstraBestFirstSearch
+from jupyddl.dijkstra import DijkstraBestFirstSearch, zero_heuristic
 from jupyddl.a_star import AStarBestFirstSearch
 from jupyddl.bfs import BreadthFirstSearch
 from jupyddl.heuristics import BasicHeuristic, DeleteRelaxationHeuristic
@@ -37,6 +37,33 @@ def test_search_dijkstra():
     assert res[-1] > 0  # Assert that it visited some nodes
 
 
+def test_search_dijkstra_no_path():
+    apla = AutomatedPlanner(
+        "pddl-examples/vehicle/domain.pddl", "pddl-examples/vehicle/problem.pddl"
+    )
+    dijk = DijkstraBestFirstSearch(apla)
+    res = dijk.search()  # Goal, computation_time, opened_nodes(in this order)
+    assert not res[0]
+
+
+def test_search_dfs_no_path():
+    apla = AutomatedPlanner(
+        "pddl-examples/vehicle/domain.pddl", "pddl-examples/vehicle/problem.pddl"
+    )
+    dfs = DepthFirstSearch(apla)
+    res = dfs.search()  # Goal, computation_time, opened_nodes(in this order)
+    assert not res[0]
+
+
+def test_search_bfs_no_path():
+    apla = AutomatedPlanner(
+        "pddl-examples/vehicle/domain.pddl", "pddl-examples/vehicle/problem.pddl"
+    )
+    bfs = BreadthFirstSearch(apla)
+    res = bfs.search()  # Goal, computation_time, opened_nodes(in this order)
+    assert not res[0]
+
+
 def test_search_astar_basic():
     apla = AutomatedPlanner(
         "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
@@ -48,15 +75,19 @@ def test_search_astar_basic():
     assert res[-1] > 0  # Assert that it visited at least one node
 
 
-def test_search_astar_delete_relaxation():
+def test_search_astar_basic_no_path():
     apla = AutomatedPlanner(
-        "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
+        "pddl-examples/vehicle/domain.pddl", "pddl-examples/vehicle/problem.pddl"
     )
-    heuristic = DeleteRelaxationHeuristic(apla, "delete_relaxation/h_max")
+    heuristic = BasicHeuristic(apla, "basic/goal_count")
     astar = AStarBestFirstSearch(apla, heuristic.compute)
     res = astar.search()  # Goal, computation_time, opened_nodes(in this order)
-    assert res[1] != 0  # Assert that it took time to compute
-    assert res[-1] > 0  # Assert that it visited at least one node
+    assert not res[0]
+
+
+def test_zero_heuristic():
+    assert zero_heuristic() == 0
+
 
 def test_search_getter_costs():
     apla = AutomatedPlanner(
@@ -69,6 +100,7 @@ def test_search_getter_costs():
 
     assert path and plan and state_plan
 
+
 def test_search_getter_no_costs():
     apla = AutomatedPlanner(
         "pddl-examples/cargo/domain.pddl", "pddl-examples/cargo/problem.pddl"
@@ -79,6 +111,3 @@ def test_search_getter_no_costs():
     state_plan = apla.get_state_def_from_path(path)
 
     assert path and plan and state_plan
-
-
-

tests/test_hsp_astar.py

@@ -8,22 +8,24 @@
 from jupyddl.a_star import AStarBestFirstSearch
 from jupyddl.heuristics import DeleteRelaxationHeuristic
 
-# def test_astar_hmax():
-#     apla = AutomatedPlanner(
-#         "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
-#     )
-#     heuristic = DeleteRelaxationHeuristic(apla, "delete_relaxation/h_max")
-#     astar = AStarBestFirstSearch(apla, heuristic.compute)
-#     assert astar.init.h_cost == heuristic.compute(apla.initial_state)
-
-
-# def test_astar_hadd():
-#     apla = AutomatedPlanner(
-#         "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
-#     )
-#     heuristic = DeleteRelaxationHeuristic(apla, "delete_relaxation/h_max")
-#     astar = AStarBestFirstSearch(apla, heuristic.compute)
-#     assert astar.init.h_cost == heuristic.compute(apla.initial_state)
+
+def test_astar_hmax():
+    apla = AutomatedPlanner(
+        "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
+    )
+    heuristic = DeleteRelaxationHeuristic(apla, "delete_relaxation/h_max")
+    astar = AStarBestFirstSearch(apla, heuristic.compute)
+    assert astar.init.h_cost == heuristic.compute(apla.initial_state)
+
+
+def test_astar_hadd():
+    apla = AutomatedPlanner(
+        "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
+    )
+    heuristic = DeleteRelaxationHeuristic(apla, "delete_relaxation/h_max")
+    astar = AStarBestFirstSearch(apla, heuristic.compute)
+    assert astar.init.h_cost == heuristic.compute(apla.initial_state)
+
 
 def test_astar_hmax_sensible_domain():
     apla = AutomatedPlanner(

tests/test_node.py

@@ -0,0 +1,44 @@
+# -*- coding: utf-8 -*-
+
+import sys
+from os import path
+
+sys.path.append(path.dirname(path.dirname(path.abspath(__file__))))
+from jupyddl.automated_planner import AutomatedPlanner
+from jupyddl.node import Node
+
+
+def test_node_equality_cost():
+    apla = AutomatedPlanner(
+        "pddl-examples/tsp/domain.pddl", "pddl-examples/tsp/problem.pddl"
+    )
+    actions = apla.available_actions(apla.initial_state)
+    if not actions:
+        assert 0 == 1
+        return
+    next_state = apla.transition(apla.initial_state, actions[0])
+    next_node = Node(next_state, apla, heuristic_based=True)
+    next_node_v2 = Node(next_state, apla)
+
+    assertion = next_node_v2 < next_node
+    assertion2 = next_node < next_node_v2
+
+    assert assertion and assertion2
+
+
+def test_node_equality_no_cost():
+    apla = AutomatedPlanner(
+        "pddl-examples/dinner/domain.pddl", "pddl-examples/dinner/problem.pddl"
+    )
+    actions = apla.available_actions(apla.initial_state)
+    if not actions:
+        assert 0 == 1
+        return
+    next_state = apla.transition(apla.initial_state, actions[0])
+    next_node = Node(next_state, apla, heuristic_based=True)
+    next_node_v2 = Node(next_state, apla)
+
+    assertion = next_node_v2 < next_node
+    assertion2 = next_node < next_node_v2
+
+    assert assertion and assertion2