Add method get_best to history class

src/tranquilo/acceptance_decision.py

@@ -25,6 +25,7 @@ def get_acceptance_decider(
         "naive_noisy": accept_naive_noisy,
         "noisy": accept_noisy,
         "classic_line_search": accept_classic_line_search,
+        "greedy": accept_greedy,
     }
 
     out = get_component(
@@ -38,6 +39,38 @@ def get_acceptance_decider(
     return out
 
 
+def accept_greedy(
+    subproblem_solution,
+    state,
+    history,
+    *,
+    wrapped_criterion,
+    min_improvement,
+):
+    """Do a greedy acceptance step for a trustregion algorithm.
+
+    Args:
+        subproblem_solution (SubproblemResult): Result of the subproblem solution.
+        state (State): Namedtuple containing the trustregion, criterion value of
+            previously accepted point, indices of model points, etc.
+        wrapped_criterion (callable): The criterion function.
+        min_improvement (float): Minimum improvement required to accept a point.
+
+    Returns:
+        AcceptanceResult
+
+    """
+    out = _accept_greedy(
+        subproblem_solution=subproblem_solution,
+        state=state,
+        history=history,
+        wrapped_criterion=wrapped_criterion,
+        min_improvement=min_improvement,
+        n_evals=1,
+    )
+    return out
+
+
 def _accept_classic(
     subproblem_solution,
     state,
@@ -239,6 +272,76 @@ def accept_classic_line_search(
     return res
 
 
+def _accept_greedy(
+    subproblem_solution,
+    state,
+    history,
+    *,
+    wrapped_criterion,
+    min_improvement,
+    n_evals,
+):
+    """Do a simple greedy acceptance step for a trustregion algorithm.
+
+    Args:
+        subproblem_solution (SubproblemResult): Result of the subproblem solution.
+        state (State): Namedtuple containing the trustregion, criterion value of
+            previously accepted point, indices of model points, etc.
+        wrapped_criterion (callable): The criterion function.
+        min_improvement (float): Minimum improvement required to accept a point.
+
+    Returns:
+        AcceptanceResult
+
+    """
+    candidate_x = subproblem_solution.x
+    candidate_index = history.add_xs(candidate_x)
+    wrapped_criterion({candidate_index: n_evals})
+
+    candidate_fval = np.mean(history.get_fvals(candidate_index))
+    actual_improvement = -(candidate_fval - state.fval)
+
+    rho = calculate_rho(
+        actual_improvement=actual_improvement,
+        expected_improvement=subproblem_solution.expected_improvement,
+    )
+
+    best_x, best_fval, best_index = history.get_best()
+
+    if best_fval < candidate_fval:
+        candidate_x = best_x
+        candidate_fval = best_fval
+        candidate_index = best_index
+        overall_improvement = -(candidate_fval - state.fval)
+    else:
+        overall_improvement = actual_improvement
+
+    is_accepted = overall_improvement >= min_improvement
+
+    if np.isfinite(candidate_fval):
+        res = _get_acceptance_result(
+            candidate_x=candidate_x,
+            candidate_fval=candidate_fval,
+            candidate_index=candidate_index,
+            rho=rho,
+            is_accepted=is_accepted,
+            old_state=state,
+            n_evals=n_evals,
+        )
+    else:
+        res = _get_acceptance_result(
+            candidate_x=state.x,
+            candidate_fval=state.fval,
+            candidate_index=state.index,
+            rho=-np.inf,
+            is_accepted=False,
+            old_state=state,
+            n_evals=n_evals,
+        )
+
+    return res
+
+
 def _accept_simple(
     subproblem_solution,
     state,

tests/test_acceptance_decision.py

@@ -4,6 +4,7 @@
 import pytest
 from tranquilo.sample_points import get_sampler
 from tranquilo.acceptance_decision import (
+    _accept_greedy,
     _accept_simple,
     _get_acceptance_result,
     calculate_rho,
@@ -82,6 +83,42 @@ def wrapped_criterion(eval_info):
     assert_array_equal(res_got.candidate_x, 1.0 + np.arange(2))
 
 
+# ======================================================================================
+# Test accept_greedy
+# ======================================================================================
+
+
+@pytest.mark.parametrize("state", states)
+def test_accept_greedy(
+    state,
+    subproblem_solution,
+):
+    history = History(functype="scalar")
+
+    idxs = history.add_xs(np.arange(10).reshape(5, 2))
+
+    history.add_evals(idxs.repeat(2), np.arange(10))
+
+    def wrapped_criterion(eval_info):
+        indices = np.array(list(eval_info)).repeat(np.array(list(eval_info.values())))
+        history.add_evals(indices, -indices)
+
+    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))
+
+
 # ======================================================================================
 # Test _get_acceptance_result
 # ======================================================================================