<div dir="ltr">We are now twenty days into the voting period. I assume ten remain?<div><br></div><div>While votes are still scarce, issues that have been raised include:</div><div><ul><li><a href="https://github.com/scikit-learn/scikit-learn/pull/16079#discussion_r589290894">The naming of "metadata"</a>. There is some support for the word "auxiliary" instead of "meta". (I agree that weight/groups are not typically what I would call metadata.)</li><li>Whether the API for setting requests should be more generic, e.g. rolled into the "tags" concept: <a href="https://github.com/scikit-learn/scikit-learn/pull/16079#issuecomment-794091868">https://github.com/scikit-learn/scikit-learn/pull/16079#issuecomment-794091868</a>.</li><li>Areas in which the WIP PR is still immature and needs substantial review: even if the SLEP is accepted, this is unlikely to be ready for 1.0, and the API (although not the structure of the model) will continue to be refined were the SLEP accepted.<br></li></ul><div>Thanks all for your considered critique and contributions.</div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sat, 27 Feb 2021 at 20:42, Joel Nothman <<a href="mailto:joel.nothman@gmail.com">joel.nothman@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi all,<div><br></div><div>Just a reminder that we are ten days into the month-long voting period, with one vote on record. Core devs, please find time to consider this proposal. Thanks to Andy's suggestion, we have added an example of the new API to the opening section:</div><div><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)"><br>This SLEP proposes an API where users can request certain metadata to be passed to its consumer by the meta-estimator it is wrapped in.</p><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">The following example illustrates the new <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">request_metadata</span></code> parameter for making scorers, the <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">request_sample_weight</span></code> estimator method, the <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">metadata</span></code> parameter replacing <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">fit_params</span></code> in <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">cross_validate</span></code>, and the automatic passing of <a href="https://scikit-learn.org/stable/glossary.html#term-groups" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">groups</span></code></a> to <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">GroupKFold</span></code> to enable nested grouped cross validation. Here, the user requests that the <a href="https://scikit-learn.org/stable/glossary.html#term-sample_weight" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">sample_weight</span></code></a> metadata key should be passed to a customised accuracy scorer (although a predefined ‘weighted_accuracy’ scorer could be introduced), and to the LogisticRegressionCV. <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">GroupKFold</span></code> requests <a href="https://scikit-learn.org/stable/glossary.html#term-groups" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">groups</span></code></a> by default.</p><div style="box-sizing:border-box;border:1px solid rgb(225,228,229);overflow-x:auto;margin:1px 0px 24px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;font-size:16px;background-color:rgb(252,252,252)"><div style="box-sizing:border-box;background:rgb(238,255,204);border:none;overflow-x:auto;margin:0px;padding:0px"><pre style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;line-height:1.4;margin-top:0px;margin-bottom:0px;padding:12px;overflow:auto"><span style="box-sizing:border-box"></span><span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">from</span> <span style="box-sizing:border-box;color:rgb(14,132,181);font-weight:bold">sklearn.metrics</span> <span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">import</span> <span style="box-sizing:border-box">accuracy_score</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">make_scorer</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">from</span> <span style="box-sizing:border-box;color:rgb(14,132,181);font-weight:bold">sklearn.model_selection</span> <span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">import</span> <span style="box-sizing:border-box">cross_validate</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">GroupKFold</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">from</span> <span style="box-sizing:border-box;color:rgb(14,132,181);font-weight:bold">sklearn.linear_model</span> <span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">import</span> <span style="box-sizing:border-box">LogisticRegressionCV</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">weighted_acc</span> <span style="box-sizing:border-box;color:rgb(102,102,102)">=</span> <span style="box-sizing:border-box">make_scorer</span><span style="box-sizing:border-box">(</span><span style="box-sizing:border-box">accuracy_score</span><span style="box-sizing:border-box">,</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">request_metadata</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">[</span><span style="box-sizing:border-box;color:rgb(64,112,160)">'sample_weight'</span><span style="box-sizing:border-box">])</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">group_cv</span> <span style="box-sizing:border-box;color:rgb(102,102,102)">=</span> <span style="box-sizing:border-box">GroupKFold</span><span style="box-sizing:border-box">()</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">lr</span> <span style="box-sizing:border-box;color:rgb(102,102,102)">=</span> <span style="box-sizing:border-box">LogisticRegressionCV</span><span style="box-sizing:border-box">(</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">cv</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">group_cv</span><span style="box-sizing:border-box">,</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">scoring</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">weighted_acc</span><span style="box-sizing:border-box">,</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span><span style="box-sizing:border-box">)</span><span style="box-sizing:border-box;color:rgb(102,102,102)">.</span><span style="box-sizing:border-box">request_sample_weight</span><span style="box-sizing:border-box">(</span><span style="box-sizing:border-box">fit</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">True</span><span style="box-sizing:border-box">)</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">cross_validate</span><span style="box-sizing:border-box">(</span><span style="box-sizing:border-box">lr</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">X</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">y</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">cv</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">group_cv</span><span style="box-sizing:border-box">,</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">metadata</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">{</span><span style="box-sizing:border-box;color:rgb(64,112,160)">'sample_weight'</span><span style="box-sizing:border-box">:</span> <span style="box-sizing:border-box">my_weights</span><span style="box-sizing:border-box">,</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box;color:rgb(64,112,160)">'groups'</span><span style="box-sizing:border-box">:</span> <span style="box-sizing:border-box">my_groups</span><span style="box-sizing:border-box">},</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">scoring</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">weighted_acc</span><span style="box-sizing:border-box">)</span></pre></div></div></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, 18 Feb 2021 at 00:08, Joel Nothman <<a href="mailto:joel.nothman@gmail.com" target="_blank">joel.nothman@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">With thanks to Alex, Adrin and Christian, we have a proposal to implement what we used to call "sample props" that should be expressive enough for us to resolve tens of issues and PRs, but will be largely unobtrusive for most current users.<div><br></div><div>Core developers, please cast your vote in <a href="https://github.com/scikit-learn/enhancement_proposals/pull/52" target="_blank">this PR</a> after considering the proposal <a href="https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep006/proposal.html" target="_blank">here</a>, which has a partial implementation in <a href="https://github.com/scikit-learn/scikit-learn/pull/16079" target="_blank">#16079</a>.</div><div><br></div><div><br></div><div>In brief, the problem we are trying to solve:</div><div><br></div><div><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">Scikit-learn has limited support for information pertaining to each sample (henceforth “sample properties”) to be passed through an estimation pipeline. The user can, for instance, pass fit parameters to all members of a FeatureUnion, or to a specified member of a Pipeline using dunder (<code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(231,76,60);overflow-x:auto"><span style="box-sizing:border-box">__</span></code>) prefixing:</p><div style="box-sizing:border-box;border:1px solid rgb(225,228,229);overflow-x:auto;margin:1px 0px 24px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;font-size:16px;background-color:rgb(252,252,252)"><div style="box-sizing:border-box;background:rgb(238,255,204);border:none;overflow-x:auto;margin:0px;padding:0px"><pre style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;line-height:1.4;margin-top:0px;margin-bottom:0px;padding:12px;overflow:auto"><span style="box-sizing:border-box"></span><span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">from</span> <span style="box-sizing:border-box;color:rgb(14,132,181);font-weight:bold">sklearn.pipeline</span> <span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">import</span> <span style="box-sizing:border-box">Pipeline</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">from</span> <span style="box-sizing:border-box;color:rgb(14,132,181);font-weight:bold">sklearn.linear_model</span> <span style="box-sizing:border-box;color:rgb(0,112,32);font-weight:bold">import</span> <span style="box-sizing:border-box">LogisticRegression</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">pipe</span> <span style="box-sizing:border-box;color:rgb(102,102,102)">=</span> <span style="box-sizing:border-box">Pipeline</span><span style="box-sizing:border-box">([(</span><span style="box-sizing:border-box;color:rgb(64,112,160)">'clf'</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box">LogisticRegression</span><span style="box-sizing:border-box">())])</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">>>> </span><span style="box-sizing:border-box">pipe</span><span style="box-sizing:border-box;color:rgb(102,102,102)">.</span><span style="box-sizing:border-box">fit</span><span style="box-sizing:border-box">([[</span><span style="box-sizing:border-box;color:rgb(32,128,80)">1</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box;color:rgb(32,128,80)">2</span><span style="box-sizing:border-box">],</span> <span style="box-sizing:border-box">[</span><span style="box-sizing:border-box;color:rgb(32,128,80)">3</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box;color:rgb(32,128,80)">4</span><span style="box-sizing:border-box">]],</span> <span style="box-sizing:border-box">[</span><span style="box-sizing:border-box;color:rgb(32,128,80)">5</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box;color:rgb(32,128,80)">6</span><span style="box-sizing:border-box">],</span>
<span style="box-sizing:border-box;color:rgb(198,93,9);font-weight:bold">... </span> <span style="box-sizing:border-box">clf__sample_weight</span><span style="box-sizing:border-box;color:rgb(102,102,102)">=</span><span style="box-sizing:border-box">[</span><span style="box-sizing:border-box;color:rgb(102,102,102)">.</span><span style="box-sizing:border-box;color:rgb(32,128,80)">5</span><span style="box-sizing:border-box">,</span> <span style="box-sizing:border-box;color:rgb(102,102,102)">.</span><span style="box-sizing:border-box;color:rgb(32,128,80)">7</span><span style="box-sizing:border-box">])</span>
</pre></div></div><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">Several other meta-estimators, such as GridSearchCV, support forwarding these fit parameters to their base estimator when fitting. Yet a number of important use cases are currently not supported.</p><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">Features we currently do not support and wish to include:</p><ul style="box-sizing:border-box;margin:0px 0px 24px;padding:0px;list-style-position:initial;line-height:24px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;font-size:16px;background-color:rgb(252,252,252)"><li style="box-sizing:border-box;list-style:disc;margin-left:24px">passing sample properties (e.g. <a href="https://scikit-learn.org/stable/glossary.html#term-sample_weight" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">sample_weight</span></code></a>) to a scorer used in cross-validation</li><li style="box-sizing:border-box;list-style:disc;margin-left:24px">passing sample properties (e.g. <a href="https://scikit-learn.org/stable/glossary.html#term-groups" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">groups</span></code></a>) to a CV splitter in nested cross validation</li><li style="box-sizing:border-box;list-style:disc;margin-left:24px">passing sample properties (e.g. <a href="https://scikit-learn.org/stable/glossary.html#term-sample_weight" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">sample_weight</span></code></a>) to some scorers and not others in a multi-metric cross-validation setup</li></ul></div><div><h2 style="box-sizing:border-box;margin-top:0px;font-family:"Roboto Slab",ff-tisa-web-pro,Georgia,Arial,sans-serif;font-size:24px;color:rgb(64,64,64);background-color:rgb(252,252,252)">Solution: Each consumer requests</h2><h2 style="box-sizing:border-box;margin-top:0px;font-family:"Roboto Slab",ff-tisa-web-pro,Georgia,Arial,sans-serif;font-size:24px;color:rgb(64,64,64);background-color:rgb(252,252,252)"></h2><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">A meta-estimator provides along to its children only what they request. A meta-estimator needs to request, on behalf of its children, any metadata that descendant consumers request.</p><p style="box-sizing:border-box;line-height:24px;margin:0px 0px 24px;font-size:16px;color:rgb(64,64,64);font-family:Lato,proxima-nova,"Helvetica Neue",Arial,sans-serif;background-color:rgb(252,252,252)">Each object that could receive metadata should have a method called <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">get_metadata_request()</span></code> which returns a dict that specifies which metadata is consumed by each of its methods (keys of this dictionary are therefore method names, e.g. <a href="https://scikit-learn.org/stable/glossary.html#term-fit" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">fit</span></code></a>, <a href="https://scikit-learn.org/stable/glossary.html#term-transform" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">transform</span></code></a> etc.). Estimators supporting weighted fitting may return <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">{}</span></code> by default, but have a method called <code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">request_sample_weight</span></code> which allows the user to specify the requested <a href="https://scikit-learn.org/stable/glossary.html#term-sample_weight" title="(in scikit-learn v0.24)" style="box-sizing:border-box;color:rgb(41,128,185);text-decoration-line:none" target="_blank"><code style="box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;background:rgb(255,255,255);border:1px solid rgb(225,228,229);padding:2px 5px;color:rgb(64,64,64);overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">sample_weight</span></code></a> in each of its methods. <code style="background:rgb(255,255,255);box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">make_scorer</span></code> accepts <code style="background:rgb(255,255,255);box-sizing:border-box;font-family:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",Courier,monospace;font-size:12px;white-space:nowrap;max-width:100%;border:1px solid rgb(225,228,229);padding:2px 5px;overflow-x:auto;font-weight:bold"><span style="box-sizing:border-box">request_metadata</span></code> as keyword parameter through which the user can specify what metadata is requested.</p></div><div><br></div><div>Regards,</div><div><br></div><div>Joel</div></div>
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