<div dir="ltr">Thanks @joel, for the guidance. I will get right on it, and hopefully have something for public consumption soon!<div><br></div><div>-M</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Sun, Aug 20, 2017 at 5:28 AM, Joel Nothman <span dir="ltr"><<a href="mailto:joel.nothman@gmail.com" target="_blank">joel.nothman@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">The idea is to take the template (<a href="https://github.com/scikit-learn-contrib/project-template" target="_blank">https://github.com/scikit-<wbr>learn-contrib/project-template</a><wbr>), build, test and document your estimator(s), and offer it to be housed within scikit-learn-contrib.</div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On 20 August 2017 at 08:36, Michael Capizzi <span dir="ltr"><<a href="mailto:mcapizzi@email.arizona.edu" target="_blank">mcapizzi@email.arizona.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div class="m_5208153021617140772m_7353748135166292644markdown-here-wrapper"><p style="margin:0px 0px 1.2em!important">Thanks @joel -</p>
<p style="margin:0px 0px 1.2em!important">I wasn’t aware of <code style="font-size:0.85em;font-family:Consolas,Inconsolata,Courier,monospace;margin:0px 0.15em;padding:0px 0.3em;white-space:pre-wrap;border:1px solid rgb(234,234,234);background-color:rgb(248,248,248);border-radius:3px;display:inline">scikit-learn-contrib</code>. Is this what you’re referring to? <a href="https://github.com/scikit-learn-contrib/scikit-learn-contrib" target="_blank">https://github.com/scikit-lear<wbr>n-contrib/scikit-learn-contrib</a></p>
<p style="margin:0px 0px 1.2em!important">If so, I don’t see any existing projects that this would fit into; could I start a new one in a <code style="font-size:0.85em;font-family:Consolas,Inconsolata,Courier,monospace;margin:0px 0.15em;padding:0px 0.3em;white-space:pre-wrap;border:1px solid rgb(234,234,234);background-color:rgb(248,248,248);border-radius:3px;display:inline">pull-request</code>?</p>
<p style="margin:0px 0px 1.2em!important">-M</p>
<div title="MDH:VGhhbmtzIEBqb2VsIC08ZGl2Pjxicj48L2Rpdj48ZGl2Pkkgd2Fzbid0IGF3YXJlIG9mIGBzY2lr
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aXY+LU08L2Rpdj4=" style="height:0;width:0;max-height:0;max-width:0;overflow:hidden;font-size:0em;padding:0;margin:0"></div></div></div><div class="m_5208153021617140772HOEnZb"><div class="m_5208153021617140772h5"><div class="gmail_extra"><br><div class="gmail_quote">On Sat, Aug 19, 2017 at 2:47 AM, Joel Nothman <span dir="ltr"><<a href="mailto:joel.nothman@gmail.com" target="_blank">joel.nothman@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto">this is the right place to ask, but I'd be more interested to see a scikit-learn-compatible implementation available, perhaps in scikit-learn-contrib more than to see it part of the main package...</div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="m_5208153021617140772m_7353748135166292644h5">On 19 Aug 2017 2:13 am, "Michael Capizzi" <<a href="mailto:mcapizzi@email.arizona.edu" target="_blank">mcapizzi@email.arizona.edu</a>> wrote:<br type="attribution"></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="m_5208153021617140772m_7353748135166292644h5"><div dir="ltr">Hi all -<div><br></div><div>Forgive me if this is the wrong place for posting this question, but I'd like to inquire about the community's interest in incorporating a new Transformer into the code base.</div><div><br></div><div>This paper ( <a href="https://nlp.stanford.edu/pubs/sidaw12_simple_sentiment.pdf" target="_blank">https://nlp.stanford.edu/pubs/<wbr>sidaw12_simple_sentiment.pdf</a> ) is a "classic" in Natural Language Processing and is often times used as a very competitive baseline. TL;DR it transforms a traditional count-based feature space into the conditional probabilities of a `Naive Bayes` classifier. These transformed features can then be used to train any linear classifier. The paper focuses on `SVM`.</div><div><br></div><div>The attached notebook has an example of the custom `Transformer` I built along with a custom `Classifier` to utilize this `Transformer` in a `multiclass` case (as the feature space transformation differs depending on the label).</div><div><br></div><div>If there is interest in the community for the inclusion of this `Transformer` and `Classifier`, I'd happily go through the official process of a `pull-request`, etc.</div><div><br></div><div>-Michael</div></div>
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