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Update gpCAM.py #1465

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12 changes: 8 additions & 4 deletions libensemble/gen_classes/gpCAM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,7 @@ def ask_numpy(self, n_trials: int) -> npt.NDArray:
input_set=np.column_stack((self.lb, self.ub)),
n=n_trials,
pop_size=n_trials,
acquisition_function="total correlation",
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This should prob be passed as option, with sensible default.

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@shuds13 shuds13 Dec 3, 2024 •
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For uncertainty reduction, the default "variance" simply finds N best points, but they may be close to each other. "total correlation" is more expensive but points found are self-avoiding, which is what we usually want.

But also there is "MINIMIZE", "MAXIMIZE" keywords, that I guess we need to incorporate.

acquisition_function="expected improvement",
max_iter=self.ask_max_iter, # Larger takes longer. gpCAM default is 20.
)["x"]
print(f"Ask time:{time.time() - start}")
Expand All @@ -85,14 +85,18 @@ def ask_numpy(self, n_trials: int) -> npt.NDArray:
def tell_numpy(self, calc_in: npt.NDArray) -> None:
if calc_in is not None:
if "x" in calc_in.dtype.names: # SH should we require x in?
self.x_new = np.atleast_2d(calc_in["x"])
self.x_new = np.expand_dims(calc_in["x"], 1)
self.y_new = np.atleast_2d(calc_in["f"]).T
nan_indices = [i for i, fval in enumerate(self.y_new) if np.isnan(fval[0])]
self.x_new = np.delete(self.x_new, nan_indices, axis=0)
self.y_new = np.delete(self.y_new, nan_indices, axis=0)

self.all_x = np.vstack((self.all_x, self.x_new))
self.all_y = np.vstack((self.all_y, self.y_new))
if len(self.all_x) == 0 and len(self.all_y) == 0:
self.all_x = self.x_new
self.all_y = self.y_new
else:
self.all_x = np.concat((self.all_x, self.x_new))
self.all_y = np.concat((self.all_y, self.y_new))

noise_var = self.noise * np.ones(len(self.all_y))
if self.my_gp is None:
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