Merge pull request #97 from PyconUK/change-output-of-heuristic

Change output of heuristic to be solution.
PyconUK · Aug 8, 2017 · dae3a8f · dae3a8f
2 parents 81922b0 + 5b111aa
commit dae3a8f
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Showing 2 changed files with 22 additions and 18 deletions.
diff --git a/src/conference_scheduler/scheduler.py b/src/conference_scheduler/scheduler.py
@@ -58,8 +58,21 @@ def heuristic(events,
 
     Returns
     -------
-    array
-        A numpy array
+    list
+        A list of tuples giving the event and slot index (for the given
+        events and slots lists) for all scheduled items.
+
+    Example
+    -------
+    For a solution where
+
+        * event 0 is scheduled in slot 1
+        * event 1 is scheduled in slot 4
+        * event 2 is scheduled in slot 5
+
+    the resulting list would be::
+
+        [(0, 1), (1, 4), (2, 5)]
     """
     X = heu.get_initial_array(events=events, slots=slots)
 
@@ -81,7 +94,7 @@ def func(array):
                       objective_function=func,
                       **objective_function_algorithm_kwargs)
 
-    return X
+    return list(zip(*np.nonzero(X)))
 
 
 def solution(events, slots, objective_function=None, solver=None, **kwargs):

diff --git a/tests/test_scheduler.py b/tests/test_scheduler.py
@@ -327,36 +327,27 @@ def test_removed_slot_schedule_difference(events, slots):
 
 def test_heuristic_solution(events, slots):
     np.random.seed(1)
-    array_solution = scheduler.heuristic(events=events, slots=slots)
+    solution = scheduler.heuristic(events=events, slots=slots)
 
-    expected_array = np.array([[0, 0, 0, 0, 1, 0, 0],
-                               [1, 0, 0, 0, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 1, 0]])
-    assert np.array_equal(array_solution, expected_array)
+    assert solution == [(0, 4), (1, 0), (2, 5)]
 
 
 def test_heuristic_solution_with_simulated_annealing(events, slots):
     np.random.seed(1)
-    array_solution = scheduler.heuristic(
+    solution = scheduler.heuristic(
         events=events,
         slots=slots,
         algorithm=heu.simulated_annealing,
         objective_function=of.capacity_demand_difference)
 
-    expected_array = np.array([[0, 0, 1, 0, 0, 0, 0],
-                               [0, 0, 0, 1, 0, 0, 0],
-                               [0, 1, 0, 0, 0, 0, 0]])
-    assert np.array_equal(array_solution, expected_array)
+    assert solution == [(0, 2), (1, 3), (2, 1)]
 
-    array_solution = scheduler.heuristic(
+    solution = scheduler.heuristic(
         events=events,
         slots=slots,
         algorithm=heu.simulated_annealing,
         initial_solution_algorithm_kwargs={"max_iterations": 10},
         objective_function_algorithm_kwargs={"max_iterations": 2},
         objective_function=of.capacity_demand_difference)
 
-    expected_array = np.array([[0, 0, 1, 0, 0, 0, 0],
-                               [0, 0, 0, 1, 0, 0, 0],
-                               [0, 0, 0, 0, 0, 0, 1]])
-    assert np.array_equal(array_solution, expected_array)
+    assert solution == [(0, 2), (1, 3), (2, 6)]