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Refinement: the trajectory ends at its best step, steps try the positional weight halved too, the final ladder must win

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Three changes to how the trajectory of commit 2bfb84bc4 moves and ends:
- A step is taken while its trial lowers R-free or R-work by more than the tie and its R-free stays within the
  tie of the best so far; the trajectory then ends at its best step. On a deposited model, whose free set it
  was fitted to, R-work falls while R-free rises, and the trajectory now ends where it began instead of walking
  on while R-work falls.
- Positional steps try the weight halved as well as doubled: at a fixed weight the geometry loosens as the model
  improves (the X-ray gradient grows with D) until the trials leave the bounds, which stopped 8dqb's
  trajectories at 2 A (angles > 2.0 deg at w0/128 by step 6).
- The final ladder's choice replaces the trajectory's end only where it displaces it (geometry within bounds,
  R-free lower by more than the tie); before, its first rung was taken by default, two more cycles of movement
  that on deposited models cost R-free.

Deposited models (14 parity sets), R-free after refinement vs refine-combined: mean +0.0058 -> +0.0016
(e.g. 7n2s +0.012 -> +0.003, 9rcs +0.013 -> -0.008, 7d98 +0.015 -> +0.003, 9yl4 +0.010 -> +0.003), the time
halved against refine-combined. MD starts, median R-free (REFMAC 0-cycle scorer), previous commit -> this:
5f6m 0.167 -> 0.168, 8dqb 0.294 -> 0.297, 7arr 0.278 -> 0.290 (REFMAC 0.311), 11if 0.244 -> 0.245, 5reo 0.258 ->
0.259, 7d98 0.335 -> 0.335. Determinism: run A == run B == --trials-at-once 1 on 5reo, 9rcs and a 5f6m MD start.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit c2b55aab1aaff637d9484dec0d25003b5c4a3857)
This commit is contained in:
leonarski_f committed 2026-10-26 01:38:24 +01:00
1 parent bda90dbdd5
commit 3b06bcee78
2 files changed
+37 -17
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image_analysis/structure_refinement/Refinement.cpp
+30 -14
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@@ -1327,8 +1327,13 @@ RefinementResult RefineModel(const gemmi::Structure &placed, const std::vector<M
slot_stage.reset();
logger.Debug("Refinement: {} weight trials at a time", trial_slots.size());
// Of a set of trials, the one kept: they come in order of trust and a later one displaces the best so far
// only where its geometry is within bounds and its R-free lower by more than the tie. -1: none ran.
// A trial displaces another only where its geometry is within bounds and its R-free lower by more than the
// tie.
auto displaces = [](const RefinementWeightTrial &t, const RefinementWeightTrial &b) {
return t.within_bounds && (!b.within_bounds || t.r_free < b.r_free - R_FREE_TIE);
};
// Of a set of trials, the one kept: they come in order of trust, the first kept unless a later one displaces
// it. -1: none ran.
auto choose = [&](std::vector<TrialOutcome> &outcomes, const char *stage) {
int best = -1;
for (size_t k = 0; k < outcomes.size(); k++) {
@@ -1337,8 +1342,7 @@ RefinementResult RefineModel(const gemmi::Structure &placed, const std::vector<M
continue;
logger.Info("Refinement, {}: {}", stage, o.line);
result.trials.push_back(o.trial);
const RefinementWeightTrial &t = o.trial, *b = best < 0 ? nullptr : &outcomes[best].trial;
if (b == nullptr || (t.within_bounds && (!b->within_bounds || t.r_free < b->r_free - R_FREE_TIE)))
if (best < 0 || displaces(o.trial, outcomes[best].trial))
best = static_cast<int>(k);
}
return best;
@@ -1359,29 +1363,41 @@ RefinementResult RefineModel(const gemmi::Structure &placed, const std::vector<M
state = std::move(outcomes[k]);
// 2. The trajectory: steps of a few macro-cycles from where the last one ended, each trying the weights as they
// are and, in turn, the positions' X-ray weight doubled and the B factors' divided and multiplied by four,
// until a step lowers neither R-work nor R-free by more than the tie.
// are and, in turn, the positions' X-ray weight halved and doubled (halved: the geometry loosens as the
// model improves at one weight, until it leaves the bounds) and the B factors' divided and multiplied by
// four.
// A step is taken while it lowers R-free or R-work by more than the tie and its R-free stays within the tie
// of the best so far; the trajectory ends at its best step (on a deposited model, whose free set it was
// fitted to, R-work falls while R-free rises, and it ends where it began).
TrialOutcome best = state;
for (int step = 0; step < settings.max_steps && state.ran && !ctx.Cancelled(); step++) {
const double w = state.trial.weight, adp = state.trial.adp_weight;
std::vector<std::pair<double, double>> trials = {{w, adp}};
if (step % 2 == 1 && adp > 0) {
trials.emplace_back(w, adp / 4);
trials.emplace_back(w, 4 * adp);
} else if (2 * w <= w_max)
trials.emplace_back(2 * w, adp);
} else {
trials.emplace_back(w / 2, adp);
if (2 * w <= w_max)
trials.emplace_back(2 * w, adp);
}
outcomes = RunTrials(trial_slots, trials, refinable, state.pos, state.b, &state.fit, settings.macro_cycles,
settings);
k = choose(outcomes, fmt::format("step {}", step + 1).c_str());
if (k < 0)
break;
const bool converged = state.trial.r_free - outcomes[k].trial.r_free < R_FREE_TIE &&
state.trial.r_work - outcomes[k].trial.r_work < R_FREE_TIE;
state = std::move(outcomes[k]);
if (converged)
const RefinementWeightTrial &t = outcomes[k].trial;
const bool gains = displaces(t, state.trial) || (t.within_bounds && t.r_work < state.trial.r_work - R_FREE_TIE);
if (!gains || !(t.within_bounds && t.r_free < best.trial.r_free + R_FREE_TIE))
break;
state = std::move(outcomes[k]);
if (displaces(state.trial, best.trial))
best = state;
}
state = std::move(best);
// 3. The final weights: a short ladder from where the trajectory ended, tighter geometry first.
// 3. The final weights: a short ladder from where the trajectory ended, tighter geometry first, its choice
// kept where it displaces that.
if (state.ran && !ctx.Cancelled()) {
const double w = state.trial.weight, adp = state.trial.adp_weight;
std::vector<std::pair<double, double>> trials;
@@ -1391,7 +1407,7 @@ RefinementResult RefineModel(const gemmi::Structure &placed, const std::vector<M
outcomes = RunTrials(trial_slots, trials, refinable, state.pos, state.b, &state.fit, settings.macro_cycles,
settings);
k = choose(outcomes, "final");
if (k >= 0)
if (k >= 0 && displaces(outcomes[k].trial, state.trial))
state = std::move(outcomes[k]);
}
for (size_t s = 1; s < trial_slots.size(); s++)