build based on 41ecbf3

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-02T04:36:03","documenter_version":"1.7.0"}}
+{"documenter":{"julia_version":"1.11.1","generation_timestamp":"2024-11-03T01:55:47","documenter_version":"1.7.0"}}

dev/anatomy_of_an_implementation/index.html

@@ -69,13 +69,13 @@
 ytrain = y[train]
 model = fit(learner, (Xtrain, ytrain))  # `fit(learner, Xtrain, ytrain)` will also work
 ŷ = predict(model, Tables.subset(X, test))</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">4-element Vector{Float64}:
- 2.6239373037077884
- 1.755953072214305
- 2.105963053837229
- 2.446771490596025</code></pre><p>Extracting coefficients:</p><pre><code class="language-julia hljs">LearnAPI.coefficients(model)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">3-element Vector{Pair{Symbol, Float64}}:
- :a =&gt; 1.694936108104231
- :b =&gt; 0.2918351312905996
- :c =&gt; 2.136056921874831</code></pre><p>Serialization/deserialization:</p><pre><code class="language-julia hljs">using Serialization
+ 2.7082628869743726
+ 2.2292552062898223
+ 2.5811545413181074
+ 1.4040428435997658</code></pre><p>Extracting coefficients:</p><pre><code class="language-julia hljs">LearnAPI.coefficients(model)</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">3-element Vector{Pair{Symbol, Float64}}:
+ :a =&gt; 1.471977554317488
+ :b =&gt; 0.16465005508530292
+ :c =&gt; 1.8610627371550819</code></pre><p>Serialization/deserialization:</p><pre><code class="language-julia hljs">using Serialization
 small_model = LearnAPI.strip(model)
 filename = tempname()
 serialize(filename, small_model)</code></pre><pre><code class="language-julia hljs">recovered_model = deserialize(filename)
@@ -126,7 +126,7 @@
 model = fit(learner, MLUtils.getobs(observations_for_fit, train))
 observations_for_predict = obs(model, X)
 ẑ = predict(model, MLUtils.getobs(observations_for_predict, test))</code></pre><pre class="documenter-example-output"><code class="nohighlight hljs ansi">4-element Vector{Float64}:
- 3.2224608469983163
- 0.9064348177254196
- 1.5556273666126617
- 1.1593745604707821</code></pre><pre><code class="language-julia hljs">@assert ẑ == ŷ</code></pre><p>For an application of <a href="../obs/#LearnAPI.obs"><code>obs</code></a> to efficient cross-validation, see <a href="../obs/#obs_workflows">here</a>.</p><hr/><p>¹ In LearnAPI.jl a <em>table</em> is any object <code>X</code> implementing the <a href="https://tables.juliadata.org/dev/">Tables.jl</a> interface, additionally satisfying <code>Tables.istable(X) == true</code> and implementing <code>DataAPI.nrow</code> (and whence <code>MLUtils.numobs</code>). Tables that are also (unnamed) tuples are disallowed.</p><p>² An implementation can provide further accessor functions, if necessary, but like the native ones, they must be included in the <a href="../traits/#LearnAPI.functions"><code>LearnAPI.functions</code></a> declaration.</p><p>³ The last index must be the observation index.</p><p>⁴ The <code>data = (X, y)</code> pattern implemented here is not the only supported pattern. For, example, <code>data</code> might be a single table containing both features and target variable. In this case, it will be necessary to overload <a href="../target_weights_features/#LearnAPI.features"><code>LearnAPI.features</code></a> in addition to <a href="../target_weights_features/#LearnAPI.target"><code>LearnAPI.target</code></a>; the name of the target column would need to be a hyperparameter.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Home</a><a class="docs-footer-nextpage" href="../reference/">Overview »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Saturday 2 November 2024 04:36">Saturday 2 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+ 2.62786647537796
+ 2.094847555114257
+ 2.3824850196428318
+ 1.9157203692614744</code></pre><pre><code class="language-julia hljs">@assert ẑ == ŷ</code></pre><p>For an application of <a href="../obs/#LearnAPI.obs"><code>obs</code></a> to efficient cross-validation, see <a href="../obs/#obs_workflows">here</a>.</p><hr/><p>¹ In LearnAPI.jl a <em>table</em> is any object <code>X</code> implementing the <a href="https://tables.juliadata.org/dev/">Tables.jl</a> interface, additionally satisfying <code>Tables.istable(X) == true</code> and implementing <code>DataAPI.nrow</code> (and whence <code>MLUtils.numobs</code>). Tables that are also (unnamed) tuples are disallowed.</p><p>² An implementation can provide further accessor functions, if necessary, but like the native ones, they must be included in the <a href="../traits/#LearnAPI.functions"><code>LearnAPI.functions</code></a> declaration.</p><p>³ The last index must be the observation index.</p><p>⁴ The <code>data = (X, y)</code> pattern implemented here is not the only supported pattern. For, example, <code>data</code> might be a single table containing both features and target variable. In this case, it will be necessary to overload <a href="../target_weights_features/#LearnAPI.features"><code>LearnAPI.features</code></a> in addition to <a href="../target_weights_features/#LearnAPI.target"><code>LearnAPI.target</code></a>; the name of the target column would need to be a hyperparameter.</p></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../">« Home</a><a class="docs-footer-nextpage" href="../reference/">Overview »</a><div class="flexbox-break"></div><p class="footer-message">Powered by <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> and the <a href="https://julialang.org/">Julia Programming Language</a>.</p></nav></div><div class="modal" id="documenter-settings"><div class="modal-background"></div><div class="modal-card"><header class="modal-card-head"><p class="modal-card-title">Settings</p><button class="delete"></button></header><section class="modal-card-body"><p><label class="label">Theme</label><div class="select"><select id="documenter-themepicker"><option value="auto">Automatic (OS)</option><option value="documenter-light">documenter-light</option><option value="documenter-dark">documenter-dark</option><option value="catppuccin-latte">catppuccin-latte</option><option value="catppuccin-frappe">catppuccin-frappe</option><option value="catppuccin-macchiato">catppuccin-macchiato</option><option value="catppuccin-mocha">catppuccin-mocha</option></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.7.0 on <span class="colophon-date" title="Sunday 3 November 2024 01:55">Sunday 3 November 2024</span>. Using Julia version 1.11.1.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>