Add test for accept_greedy

tests/test_acceptance_decision.py

@@ -1,10 +1,11 @@
 from collections import namedtuple
 
+from functools import partial
 import numpy as np
 import pytest
 from tranquilo.sample_points import get_sampler
 from tranquilo.acceptance_decision import (
-    _accept_greedy,
+    accept_greedy,
     _accept_simple,
     _get_acceptance_result,
     calculate_rho,
@@ -93,30 +94,46 @@ def test_accept_greedy(
     state,
     subproblem_solution,
 ):
+    """Test accept greedy.
+
+    Tests that the best point is chosen in the acceptance step, even though it is added
+    to the history before the acceptance step.
+
+    """
     history = History(functype="scalar")
 
-    idxs = history.add_xs(np.arange(10).reshape(5, 2))
+    def criterion(x):
+        return np.sum(x**2)
 
-    history.add_evals(idxs.repeat(2), np.arange(10))
+    def _wrapped_criterion(eval_info, history):
+        for x_index, _ in eval_info.items():
+            xs = history.get_xs(x_index)
+            crit_value = criterion(xs)
+            history.add_evals(np.array([x_index]), crit_value)
 
-    def wrapped_criterion(eval_info):
-        indices = np.array(list(eval_info)).repeat(np.array(list(eval_info.values())))
-        history.add_evals(indices, -indices)
+    wrapped_criterion = partial(_wrapped_criterion, history=history)
 
-    res_got = _accept_greedy(
+    # Add existing xs to history and evaluate wrapped criterion
+    existing_xs = np.zeros((1, 2))
+    existing_xs_indices = history.add_xs(existing_xs)
+
+    eval_info = {x_index: 1 for x_index in existing_xs_indices}
+    wrapped_criterion(eval_info)
+
+    res_got = accept_greedy(
         subproblem_solution=subproblem_solution,
         state=state,
         history=history,
         wrapped_criterion=wrapped_criterion,
         min_improvement=0.0,
-        n_evals=2,
     )
 
     assert res_got.accepted
-    assert res_got.index == 5
-    assert res_got.candidate_index == 5
-    assert_array_equal(res_got.x, subproblem_solution.x)
-    assert_array_equal(res_got.candidate_x, 1.0 + np.arange(2))
+    assert res_got.index == 0
+    assert res_got.candidate_index == 0
+    assert res_got.fval == 0.0
+    assert_array_equal(res_got.x, np.zeros(2))
+    assert_array_equal(res_got.candidate_x, np.zeros(2))
 
 
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