Fix to bootstrap of free energy surfaces, affecting timing and quantitative results #535
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…changed indices, taking too long.
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Couple more changes I will put in to fix uncertainties, don't do anything yet to it . . . |
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OK, I think I got the changes in I needed to. |
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@mrshirts when you are ready for review go ahead and add whoever you want to review this PR 😄 |
| self.u_kn[:, bootstrap_indices], self.N_k, initial_f_k=self.mbar.f_k | ||
| ) | ||
| x_nb = x_n[bootstrap_indices] | ||
| # recompute MBAR. |
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This was unnecessary - it was running MBAR too many times. This saves approximately 2X time.
| fall[:, b] = h["f"] - h["f"][j] | ||
| df_i = np.std(fall, axis=1) | ||
| fall[:, b] = h["f"] - h["f"][j] # subtract out the reference bin | ||
| df_i = np.std(fall, ddof=1, axis=1) |
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Fixing the std definition.
| @@ -1510,7 +1512,7 @@ def _get_fes_histogram( | |||
| fall[i, j, b] = ( | |||
| histogram_datas[b]["f"] - histogram_datas[b]["f"].transpose() | |||
| ) | |||
| dfxij_vals = np.std(fall, axis=2) | |||
| dfxij_vals = np.std(fall, ddof=1, axis=2) | |||
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Fixing std definition
| kde = self.kde | ||
| kde.fit(x_n, sample_weight=self.w_n) | ||
| kde = self.kde # use these new weights for the KDE | ||
| w_n = self.w_n |
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I actually can't remember if this was 100% necessary to get updated weights . . .
| fall[:, b] = h["f"] - h["f"][j] | ||
| df_i = np.std(fall, axis=1) | ||
| fall[:, b] = h["f"] - h["f"][j] # subtract out the reference bin | ||
| df_i = np.std(fall, ddof=1, axis=1) |
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Fix bootstrap std definition
| @@ -1565,9 +1567,15 @@ def _get_fes_kde( | |||
| if reference_point == "from-lowest": | |||
| fmin = np.min(f_i) | |||
| f_i = f_i - fmin | |||
| wheremin = np.argmin( | |||
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Need to find the location that this is zeroed at for the actual computation of the std.
| elif reference_point == "from-specified": | ||
| fmin = -self.kde.score_samples(np.array(fes_reference).reshape(1, -1)) | ||
| f_i = f_i - fmin | ||
| wheremin = np.argmin( |
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Need to find the location that this is zeroed at for the actual computation of the std.
| fall[:, b] = -self.kdes[b].score_samples(x) - fmin | ||
| df_i = np.std(fall, axis=1) | ||
| fall[:, b] = -self.kdes[b].score_samples(x) | ||
| fall[:, b] -= fall[wheremin, b] |
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Zero out at the correct location.
mrshirts
changed the title
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Suggestions for anyone else who should review - or if there's anyone who could take a look? We are looking at some free energy surface problems for OpenFF, so we want to get this through. |
Free energy surface code was calling MBAR after each call to randomizing bootstraps. It does not appear to affect the results, but slows things down by a factor of a little less than 2x (146 vs 87 seconds for one sample run).