Strategy selector in documentation

docs/javascripts/selectortabs.js

@@ -0,0 +1,100 @@
+// set default strategy
+let problemDescription = new Map([
+    ["objective", "single-objective"],
+    ["tasks", "single-task"],
+    ["domain", "simple-domain"],
+])
+
+const strategyMap = new Map([
+    ["single-objective__single-task__simple-domain", ["SoboStrategy", ""]],
+    ["single-objective__single-task__many-categorical-features", ["EntingStrategy", ""]],
+    ["single-objective__multi-task__simple-domain", ["SoboStrategy", "MultiTaskGPSurrogate"]],
+    ["single-objective__multi-fidelity__simple-domain", ["MultiFidelity", ""]],
+
+    ["multi-objective__single-task__simple-domain", ["MoboStrategy", ""]],
+])
+
+const getSurrogateCode = (surrogateDataModel) => {
+    if (surrogateDataModel === "") {
+        return ""
+    }
+    return `
+# define the surrogate data model
+surrogate_data_model = BotorchSurrogates(surrogates=[
+    ${surrogateDataModel}(
+        inputs=domain.inputs,
+        outputs=domain.outputs,
+    )
+])
+`}
+
+const getStrategyComment = (strategyDataModel) => {
+    return (strategyDataModel !== "EntingStrategy") ? "" : `
+# the default GP surrogate is slow to optimize when many discrete features are present
+# the ENTMOOT model can optimize over domains with many categories`
+}
+
+const getStrategyCode = () => {
+    const dataModels = strategyMap.get([...problemDescription.values()].join("__"))
+    if (dataModels === undefined) {
+        return hljs.highlight("# There isn't currently a BoFire model that works for this problem.", {language: "python"})
+    }
+
+    const [strategyDataModel, surrogateDataModel] = dataModels
+    const requiresSurrogate = (surrogateDataModel !== "")
+    const surrogateImport = (!requiresSurrogate) ? "" : `
+from bofire.data_models.surrogates.api import ${surrogateDataModel}`
+
+    const strategyComment = getStrategyComment(strategyDataModel)
+    const surrogateCode = getSurrogateCode(surrogateDataModel)
+
+    return hljs.highlight(
+`from bofire.data_models.strategies.api import ${strategyDataModel} ${surrogateImport}
+import bofire.strategies.api as strategies
+${surrogateCode}
+# define the strategy data model ${strategyComment}
+strategy_data_model = ${strategyDataModel}(
+    domain=domain${!requiresSurrogate ? "" : `
+    surrogate=surrogate_data_model`}
+)
+
+# create an instance of the functional strategy
+# to understand the difference between data models and functional components,
+# see https://experimental-design.github.io/bofire/data_models_functionals/
+strategy = strategies.map(strategy_data_model)
+`, { language : "python"}
+    )
+}
+
+const updateStrategyCodeBlock = () => {
+    const codeBlock = document.getElementById("stategyTemplate")
+    codeBlock.innerHTML = getStrategyCode().value
+}
+
+const tabSync = () => {
+    const tabs = document.querySelectorAll(".tabbed-set > input")
+    for (const tab of tabs) {
+      tab.addEventListener("click", () => {
+        const current = document.querySelector(`label[for=${tab.id}]`)
+        const pos = current.getBoundingClientRect().top
+        // const labels = document.querySelectorAll('.tabbed-set > label, .tabbed-alternate > .tabbed-labels > label')
+
+        const updatedGroup = tab.parentElement.id
+        // const updatedGroup = selectorTabStrategies[tab.id]
+        problemDescription.set(updatedGroup, tab.id)
+        updateStrategyCodeBlock()
+
+        // Preserve scroll position
+        const delta = (current.getBoundingClientRect().top) - pos
+        window.scrollBy(0, delta)
+      })
+    }
+  }
+
+document$.subscribe(function() {
+    ["objective", "tasks", "domain"].forEach(k => {
+        document.querySelector(`#${k}-marker`).nextElementSibling.setAttribute("id", k)
+    })
+    tabSync()
+    updateStrategyCodeBlock()
+})

docs/userguide_strategies.md

@@ -0,0 +1,33 @@
+# Strategies
+
+<!-- 
+    https://github.com/sgbaird/honegumi 
+    https://github.com/facebook/Ax/issues/1479
+-->
+
+In BoFire, a `Strategy` provides an interface for proposing new experiments. 
+Below, we provide a tool for suggesting the most appropriate strategy for your
+Bayesian optimization problem. Our tool is inspired by [honegumi](https://github.com/sgbaird/honegumi).
+
+<div class="tab-marker" id="objective-marker"> </div>
+
+=== "Single Objective"
+
+=== "Multi Objective"
+
+<div class="tab-marker" id="tasks-marker"> </div>
+
+===! "Single Task"
+
+=== "Multi Task"
+
+=== "Multi Fidelity"
+
+<div class="tab-marker" id="domain-marker"> </div>
+
+===! "Simple Domain"
+
+=== "Many categorical features"
+
+
+<pre><code id="stategyTemplate"> </ code><pre>

mkdocs.yaml

@@ -9,6 +9,7 @@ nav:
   - Notebook page: getting_started.ipynb
   - Examples: examples.md
   - Data Models vs Functional Components: data_models_functionals.md
+  - Strategies: userguide_strategies.md
   - Surrogate Models: userguide_surrogates.md
   - API Reference:
     - Domain: ref-domain.md
@@ -60,10 +61,18 @@ markdown_extensions:
   - pymdownx.details
   - pymdownx.highlight
   - pymdownx.inlinehilite
+  - pymdownx.tabbed:
+      alternate_style: true
+      slugify: !!python/object/apply:pymdownx.slugs.slugify {kwds: {case: lower}}
   - admonition
 
+extra_css:
+  - https://cdn.jsdelivr.net/gh/highlightjs/cdn-release@11.11.1/build/styles/default.min.css
+
 extra_javascript:
+  - https://cdn.jsdelivr.net/gh/highlightjs/cdn-release@latest/build/highlight.min.js
   - javascripts/mathjax.js
+  - javascripts/selectortabs.js
   - https://unpkg.com/mathjax@3/es5/tex-mml-chtml.js
 
 extra: