build based on 4751c20

dev/.documenter-siteinfo.json

@@ -1 +1 @@
-{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-24T22:34:44","documenter_version":"1.4.1"}}
+{"documenter":{"julia_version":"1.10.3","generation_timestamp":"2024-05-28T05:18:13","documenter_version":"1.4.1"}}

dev/index.html

@@ -46,4 +46,4 @@
 using AutoMLPipeline.SKPreprocessors 
 using AutoMLPipeline.Utils`</code></pre><p>CSV and DataFrames will be needed in the succeeding examples and should be installed:</p><pre><code class="language-julia hljs">using Pkg
 Pkg.add(&quot;CSV&quot;)
-Pkg.add(&quot;DataFrames&quot;)</code></pre><h2 id="Tutorial-Outline"><a class="docs-heading-anchor" href="#Tutorial-Outline">Tutorial Outline</a><a id="Tutorial-Outline-1"></a><a class="docs-heading-anchor-permalink" href="#Tutorial-Outline" title="Permalink"></a></h2><ul><li><a href="tutorial/pipeline/#PipelineUsage">Pipeline</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/pipeline/#Dataset">Dataset</a></li><li><a href="tutorial/pipeline/#AutoMLPipeline-Modules-and-Instances">AutoMLPipeline Modules and Instances</a></li><li><a href="tutorial/pipeline/#Processing-Categorical-Features">Processing Categorical Features</a></li><li><a href="tutorial/pipeline/#Processing-Numerical-Features">Processing Numerical Features</a></li><li><a href="tutorial/pipeline/#Concatenating-Extracted-Categorical-and-Numerical-Features">Concatenating Extracted Categorical and Numerical Features</a></li><li><a href="tutorial/pipeline/#Performance-Evaluation-of-the-Pipeline">Performance Evaluation of the Pipeline</a></li><li><a href="tutorial/pipeline/#Learners-as-Filters">Learners as Filters</a></li><li><a href="tutorial/pipeline/#Advanced-Expressions-using-Selector-Pipeline">Advanced Expressions using Selector Pipeline</a></li></ul></li></ul></li><li><a href="tutorial/preprocessing/#Preprocessing">Preprocessing</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/preprocessing/#CatNumDiscriminator-for-Detecting-Categorical-Numeric-Features">CatNumDiscriminator for Detecting Categorical Numeric Features</a></li><li><a href="tutorial/preprocessing/#Concatenating-Hot-Bits-with-PCA-of-Numeric-Columns">Concatenating Hot-Bits with PCA of Numeric Columns</a></li><li><a href="tutorial/preprocessing/#Performance-Evaluation">Performance Evaluation</a></li></ul></li></ul></li><li><a href="tutorial/learning/#Training-and-Validation">Training and Validation</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/learning/#Evaluate-Learners-with-Same-Pipeline">Evaluate Learners with Same Pipeline</a></li></ul></li></ul></li><li><a href="tutorial/extending/#Extending-AutoMLPipeline">Extending AutoMLPipeline</a></li></ul><h2 id="Manual-Outline"><a class="docs-heading-anchor" href="#Manual-Outline">Manual Outline</a><a id="Manual-Outline-1"></a><a class="docs-heading-anchor-permalink" href="#Manual-Outline" title="Permalink"></a></h2><ul><li><a href="man/pipeline/#Pipeline">Pipeline</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/pipeline/#Pipeline-Structure">Pipeline Structure</a></li><li><a href="man/pipeline/#ComboPipeline-Structure">ComboPipeline Structure</a></li><li><a href="man/pipeline/#Selector-Pipeline-Structure">Selector Pipeline Structure</a></li><li><a href="man/pipeline/#Macro-Functions">Macro Functions</a></li></ul></li></ul></li><li><a href="man/preprocessors/#Preprocessors">Preprocessors</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/preprocessors/#SKPreprocessor-Structure">SKPreprocessor Structure</a></li></ul></li></ul></li><li><a href="man/learners/#Learners">Learners</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/learners/#SKLearner-Structure">SKLearner Structure</a></li></ul></li></ul></li><li><a href="man/metaensembles/#Ensemble-Methods">Ensemble Methods</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/metaensembles/#StackEnsemble">StackEnsemble</a></li><li><a href="man/metaensembles/#VoteEnsemble">VoteEnsemble</a></li><li><a href="man/metaensembles/#bestlearner">BestLearner</a></li></ul></li></ul></li></ul><h2 id="ML-Library"><a class="docs-heading-anchor" href="#ML-Library">ML Library</a><a id="ML-Library-1"></a><a class="docs-heading-anchor-permalink" href="#ML-Library" title="Permalink"></a></h2><ul><li class="no-marker"><ul><li><a href="lib/typesfunctions/#Index">Index</a></li><li><a href="lib/typesfunctions/#Descriptions">Descriptions</a></li></ul></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="tutorial/pipeline/">Pipeline »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Friday 24 May 2024 22:34">Friday 24 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+Pkg.add(&quot;DataFrames&quot;)</code></pre><h2 id="Tutorial-Outline"><a class="docs-heading-anchor" href="#Tutorial-Outline">Tutorial Outline</a><a id="Tutorial-Outline-1"></a><a class="docs-heading-anchor-permalink" href="#Tutorial-Outline" title="Permalink"></a></h2><ul><li><a href="tutorial/pipeline/#PipelineUsage">Pipeline</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/pipeline/#Dataset">Dataset</a></li><li><a href="tutorial/pipeline/#AutoMLPipeline-Modules-and-Instances">AutoMLPipeline Modules and Instances</a></li><li><a href="tutorial/pipeline/#Processing-Categorical-Features">Processing Categorical Features</a></li><li><a href="tutorial/pipeline/#Processing-Numerical-Features">Processing Numerical Features</a></li><li><a href="tutorial/pipeline/#Concatenating-Extracted-Categorical-and-Numerical-Features">Concatenating Extracted Categorical and Numerical Features</a></li><li><a href="tutorial/pipeline/#Performance-Evaluation-of-the-Pipeline">Performance Evaluation of the Pipeline</a></li><li><a href="tutorial/pipeline/#Learners-as-Filters">Learners as Filters</a></li><li><a href="tutorial/pipeline/#Advanced-Expressions-using-Selector-Pipeline">Advanced Expressions using Selector Pipeline</a></li></ul></li></ul></li><li><a href="tutorial/preprocessing/#Preprocessing">Preprocessing</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/preprocessing/#CatNumDiscriminator-for-Detecting-Categorical-Numeric-Features">CatNumDiscriminator for Detecting Categorical Numeric Features</a></li><li><a href="tutorial/preprocessing/#Concatenating-Hot-Bits-with-PCA-of-Numeric-Columns">Concatenating Hot-Bits with PCA of Numeric Columns</a></li><li><a href="tutorial/preprocessing/#Performance-Evaluation">Performance Evaluation</a></li></ul></li></ul></li><li><a href="tutorial/learning/#Training-and-Validation">Training and Validation</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="tutorial/learning/#Evaluate-Learners-with-Same-Pipeline">Evaluate Learners with Same Pipeline</a></li></ul></li></ul></li><li><a href="tutorial/extending/#Extending-AutoMLPipeline">Extending AutoMLPipeline</a></li></ul><h2 id="Manual-Outline"><a class="docs-heading-anchor" href="#Manual-Outline">Manual Outline</a><a id="Manual-Outline-1"></a><a class="docs-heading-anchor-permalink" href="#Manual-Outline" title="Permalink"></a></h2><ul><li><a href="man/pipeline/#Pipeline">Pipeline</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/pipeline/#Pipeline-Structure">Pipeline Structure</a></li><li><a href="man/pipeline/#ComboPipeline-Structure">ComboPipeline Structure</a></li><li><a href="man/pipeline/#Selector-Pipeline-Structure">Selector Pipeline Structure</a></li><li><a href="man/pipeline/#Macro-Functions">Macro Functions</a></li></ul></li></ul></li><li><a href="man/preprocessors/#Preprocessors">Preprocessors</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/preprocessors/#SKPreprocessor-Structure">SKPreprocessor Structure</a></li></ul></li></ul></li><li><a href="man/learners/#Learners">Learners</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/learners/#SKLearner-Structure">SKLearner Structure</a></li></ul></li></ul></li><li><a href="man/metaensembles/#Ensemble-Methods">Ensemble Methods</a></li><li class="no-marker"><ul><li class="no-marker"><ul><li><a href="man/metaensembles/#StackEnsemble">StackEnsemble</a></li><li><a href="man/metaensembles/#VoteEnsemble">VoteEnsemble</a></li><li><a href="man/metaensembles/#bestlearner">BestLearner</a></li></ul></li></ul></li></ul><h2 id="ML-Library"><a class="docs-heading-anchor" href="#ML-Library">ML Library</a><a id="ML-Library-1"></a><a class="docs-heading-anchor-permalink" href="#ML-Library" title="Permalink"></a></h2><ul><li class="no-marker"><ul><li><a href="lib/typesfunctions/#Index">Index</a></li><li><a href="lib/typesfunctions/#Descriptions">Descriptions</a></li></ul></li></ul></article><nav class="docs-footer"><a class="docs-footer-nextpage" href="tutorial/pipeline/">Pipeline »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Tuesday 28 May 2024 05:18">Tuesday 28 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>

dev/man/learners/index.html

@@ -8,22 +8,22 @@
     )
 
 Helper Function:
-    SKLearner(learner::String,args::Dict=Dict())</code></pre><p>SKLearner maintains a dictionary of learners which can be listed by invoking the function: <code>sklearners()</code> The <code>:impl_args</code> is a dictionary of paramters to be passed as arguments to the Scikitlearn learner.</p><p>Let&#39;s try loading some learners with some arguments based on Scikitlearn documentation:</p><pre><code class="language-julia-repl hljs" style="display:block;">julia&gt; using AutoMLPipeline</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; iris = getiris();</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; X = iris[:,1:4];</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; Y = iris[:,end] |&gt; Vector;</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; rf = SKLearner(&quot;RandomForestClassifier&quot;,Dict(:n_estimators=&gt;30,:random_state=&gt;0));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(rf,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">fold: 1, 0.92
-fold: 2, 0.98
-fold: 3, 1.0
+    SKLearner(learner::String,args::Dict=Dict())</code></pre><p>SKLearner maintains a dictionary of learners which can be listed by invoking the function: <code>sklearners()</code> The <code>:impl_args</code> is a dictionary of paramters to be passed as arguments to the Scikitlearn learner.</p><p>Let&#39;s try loading some learners with some arguments based on Scikitlearn documentation:</p><pre><code class="language-julia-repl hljs" style="display:block;">julia&gt; using AutoMLPipeline</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; iris = getiris();</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; X = iris[:,1:4];</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; Y = iris[:,end] |&gt; Vector;</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; rf = SKLearner(&quot;RandomForestClassifier&quot;,Dict(:n_estimators=&gt;30,:random_state=&gt;0));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(rf,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">fold: 1, 0.94
+fold: 2, 0.96
+fold: 3, 0.98
 errors: 0
-(mean = 0.9666666666666667, std = 0.041633319989322626, folds = 3, errors = 0)</code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; ada = SKLearner(&quot;AdaBoostClassifier&quot;,Dict(:n_estimators=&gt;20,:random_state=&gt;0));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(ada,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">/home/runner/work/AutoMLPipeline.jl/AutoMLPipeline.jl/docs/.CondaPkg/env/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
+(mean = 0.96, std = 0.020000000000000018, folds = 3, errors = 0)</code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; ada = SKLearner(&quot;AdaBoostClassifier&quot;,Dict(:n_estimators=&gt;20,:random_state=&gt;0));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(ada,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">/home/runner/work/AutoMLPipeline.jl/AutoMLPipeline.jl/docs/.CondaPkg/env/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
   warnings.warn(
 fold: 1, 0.9
 /home/runner/work/AutoMLPipeline.jl/AutoMLPipeline.jl/docs/.CondaPkg/env/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
   warnings.warn(
-fold: 2, 0.96
+fold: 2, 0.94
 /home/runner/work/AutoMLPipeline.jl/AutoMLPipeline.jl/docs/.CondaPkg/env/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
   warnings.warn(
-fold: 3, 0.92
+fold: 3, 0.98
 errors: 0
-(mean = 0.9266666666666666, std = 0.030550504633038898, folds = 3, errors = 0)</code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; svc = SKLearner(&quot;SVC&quot;,Dict(:kernel=&gt;&quot;rbf&quot;,:random_state=&gt;0,:gamma=&gt;&quot;auto&quot;));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(svc,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">fold: 1, 0.94
-fold: 2, 0.96
+(mean = 0.94, std = 0.03999999999999998, folds = 3, errors = 0)</code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; svc = SKLearner(&quot;SVC&quot;,Dict(:kernel=&gt;&quot;rbf&quot;,:random_state=&gt;0,:gamma=&gt;&quot;auto&quot;));</code><code class="nohighlight hljs ansi" style="display:block;"></code><br/><code class="language-julia-repl hljs" style="display:block;">julia&gt; crossvalidate(svc,X,Y,&quot;accuracy_score&quot;,3)</code><code class="nohighlight hljs ansi" style="display:block;">fold: 1, 0.9
+fold: 2, 0.98
 fold: 3, 0.98
 errors: 0
-(mean = 0.96, std = 0.020000000000000018, folds = 3, errors = 0)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../preprocessors/">« Preprocessors</a><a class="docs-footer-nextpage" href="../metaensembles/">Meta-Ensembles »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Friday 24 May 2024 22:34">Friday 24 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>
+(mean = 0.9533333333333333, std = 0.046188021535170036, folds = 3, errors = 0)</code></pre></article><nav class="docs-footer"><a class="docs-footer-prevpage" href="../preprocessors/">« Preprocessors</a><a class="docs-footer-nextpage" href="../metaensembles/">Meta-Ensembles »</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></select></div></p><hr/><p>This document was generated with <a href="https://github.com/JuliaDocs/Documenter.jl">Documenter.jl</a> version 1.4.1 on <span class="colophon-date" title="Tuesday 28 May 2024 05:18">Tuesday 28 May 2024</span>. Using Julia version 1.10.3.</p></section><footer class="modal-card-foot"></footer></div></div></div></body></html>