Proof-read introduction vignette - #7
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| `ObjectiveRFun` evaluates one configuration at a time; `ObjectiveRFunDt` is the batch-wise variant for functions that can process a whole `data.table` of configurations at once. | ||
| Codomains with `"minimize"` or `"maximize"` tags remain admissible -- the same machinery can drive model-based optimization -- but this vignette concentrates on the pure learning case. | ||
| Codomains with `"minimize"` or `"maximize"` tags remain admissible---the same machinery can drive model-based optimization---but this vignette concentrates on the pure learning case. |
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Does celecx handle "minimize" or "maximize" in any way? If not, I think this is unnecessary.
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| The batches take turns between the two difficult parts of the function---the oscillatory left, whose fine structure the model cannot yet interpolate, and the steep right flank---because each refit moves the widest part of the credible band elsewhere. | ||
| Within a batch the four points cluster tightly, a consequence of greedy batch construction that Section 3.3 returns to. | ||
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| ```{r first-plot, fig.height = 4, fig.cap = "Figure 1: Active learning on the one-dimensional test function. Points show evaluated configurations, shaded by batch; the shaded region is the emulator's 95% credible band after the final batch."} |
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Do we want to hide the figure-generating code?
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New Fig. 1 legend |
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| col = experiment_colors[archive_experiment$batch_nr]) | ||
| plot(progress$data$batch_nr, progress$data$mae, log = "y", type = "b", pch = 19, | ||
| xlab = "batch", ylab = "held-out MAE", main = "Learning curve") | ||
| par(old_par) |
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For some reason, shown as a separate code field in with pkgdown.
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I like the structure of the vignette and it all follows quite logically from itself. Having read it a couple of times, however, I still get confused which class is for what purpose. A short overview might help.
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