-
-
Notifications
You must be signed in to change notification settings - Fork 1
/
README.Rmd
136 lines (100 loc) · 4.26 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
---
output: github_document
---
```{r, include = FALSE}
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")
```
# mlr3mbo
Package website: [release](https://mlr3mbo.mlr-org.com/) | [dev](https://mlr3mbo.mlr-org.com/dev/)
<!-- badges: start -->
[](https://github.com/mlr-org/mlr3mbo/actions/workflows/r-cmd-check.yml)
[](https://cran.r-project.org/package=mlr3mbo)
[](https://stackoverflow.com/questions/tagged/mlr3)
[](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
<!-- badges: end -->
A new R6 and much more modular implementation for single- and multi-objective Bayesian Optimization.
## Get Started
The best entry point to get familiar with `mlr3mbo` is provided via the [Bayesian Optimization](https://mlr3book.mlr-org.com/chapters/chapter5/advanced_tuning_methods_and_black_box_optimization.html#sec-bayesian-optimization) chapter in the `mlr3book`.
## Design
`mlr3mbo` is built modular relying on the following [R6](https://cran.r-project.org/package=R6) classes:
* `Surrogate`: Surrogate Model
* `AcqFunction`: Acquisition Function
* `AcqOptimizer`: Acquisition Function Optimizer
Based on these, Bayesian Optimization (BO) loops can be written, see, e.g., `bayesopt_ego` for sequential single-objective BO.
`mlr3mbo` also provides an `OptimizerMbo` class behaving like any other `Optimizer` from the [bbotk](https://cran.r-project.org/package=bbotk) package as well as
a `TunerMbo` class behaving like any other `Tuner` from the [mlr3tuning](https://cran.r-project.org/package=mlr3tuning) package.
`mlr3mbo` uses sensible defaults for the `Surrogate`, `AcqFunction`, `AcqOptimizer`, and even the `loop_function`.
See `?mbo_defaults` for more details.
## Simple Optimization Example
Minimize the two-dimensional Branin function via sequential BO using a GP as surrogate and EI as acquisition function optimized via a local serch:
```{r, message = FALSE}
library(bbotk)
library(mlr3mbo)
library(mlr3learners)
set.seed(1)
fun = function(xdt) {
y = branin(xdt[["x1"]], xdt[["x2"]])
data.table(y = y)
}
domain = ps(
x1 = p_dbl(-5, 10),
x2 = p_dbl(0, 15)
)
codomain = ps(
y = p_dbl(tags = "minimize")
)
objective = ObjectiveRFunDt$new(
fun = fun,
domain = domain,
codomain = codomain
)
instance = oi(
objective = objective,
terminator = trm("evals", n_evals = 25)
)
surrogate = srlrn(lrn("regr.km", control = list(trace = FALSE)))
acq_function = acqf("ei")
acq_optimizer = acqo(
opt("local_search", n_initial_points = 10, initial_random_sample_size = 1000, neighbors_per_point = 10),
terminator = trm("evals", n_evals = 2000)
)
optimizer = opt("mbo",
loop_function = bayesopt_ego,
surrogate = surrogate,
acq_function = acq_function,
acq_optimizer = acq_optimizer
)
optimizer$optimize(instance)
```
We can quickly visualize the contours of the objective function (on log scale) as well as the sampling behavior of our BO run (lighter blue colours indicating points that were evaluated in later stages of the optimization process; the first batch is given by the initial design).
```{r, eval = FALSE}
library(ggplot2)
grid = generate_design_grid(instance$search_space, resolution = 1000L)$data
grid[, y := branin(x1 = x1, x2 = x2)]
ggplot(aes(x = x1, y = x2, z = log(y)), data = grid) +
geom_contour(colour = "black") +
geom_point(aes(x = x1, y = x2, colour = batch_nr), data = instance$archive$data) +
labs(x = expression(x[1]), y = expression(x[2])) +
theme_minimal() +
theme(legend.position = "bottom")
```
Note that you can also use `bb_optimize` as a shorthand instead of constructing an optimization instance.
## Simple Tuning Example
```{r, message = FALSE}
library(mlr3)
library(mlr3learners)
library(mlr3tuning)
library(mlr3mbo)
set.seed(1)
task = tsk("pima")
learner = lrn("classif.rpart", cp = to_tune(lower = 1e-04, upper = 1, logscale = TRUE))
instance = tune(
tuner = tnr("mbo"),
task = task,
learner = learner,
resampling = rsmp("holdout"),
measure = msr("classif.ce"),
term_evals = 10)
instance$result
```