<html><head></head><body><div style="color:#000; background-color:#fff; font-family:times new roman, new york, times, serif;font-size:13px">sklearn-compiledtrees==1.3<br id="yui_3_16_0_ym19_1_1470947012971_15178"><br><div id="yui_3_16_0_ym19_1_1470947012971_15130" class="qtdSeparateBR"><br></div><div style="display: block;" id="yui_3_16_0_ym19_1_1470947012971_15138" class="yahoo_quoted"> <div id="yui_3_16_0_ym19_1_1470947012971_15137" style="font-family: times new roman, new york, times, serif; font-size: 13px;"> <div id="yui_3_16_0_ym19_1_1470947012971_15136" style="font-family: HelveticaNeue, Helvetica Neue, Helvetica, Arial, Lucida Grande, sans-serif; font-size: 16px;"> <div id="yui_3_16_0_ym19_1_1470947012971_15135" dir="ltr"> <font id="yui_3_16_0_ym19_1_1470947012971_15139" face="Arial" size="2"> <hr size="1"> <b id="yui_3_16_0_ym19_1_1470947012971_15173"><span id="yui_3_16_0_ym19_1_1470947012971_15172" style="font-weight:bold;">Von:</span></b> Maciek Wójcikowski <maciek@wojcikowski.pl><br> <b id="yui_3_16_0_ym19_1_1470947012971_15171"><span id="yui_3_16_0_ym19_1_1470947012971_15170" style="font-weight: bold;">An:</span></b> Ali Zude <zude07@yahoo.com>; Scikit-learn user and developer mailing list <scikit-learn@python.org> <br> <b id="yui_3_16_0_ym19_1_1470947012971_15169"><span id="yui_3_16_0_ym19_1_1470947012971_15168" style="font-weight: bold;">Gesendet:</span></b> 7:30 Freitag, 12.August 2016<br> <b id="yui_3_16_0_ym19_1_1470947012971_15194"><span id="yui_3_16_0_ym19_1_1470947012971_15193" style="font-weight: bold;">Betreff:</span></b> Re: [scikit-learn] Compiled trees<br> </font> </div> <div id="yui_3_16_0_ym19_1_1470947012971_15140" class="y_msg_container"><br><div id="yiv6902441652"><div id="yui_3_16_0_ym19_1_1470947012971_15142"><div id="yui_3_16_0_ym19_1_1470947012971_15141" dir="ltr">Which version of compiledtrees are you using?</div><div id="yui_3_16_0_ym19_1_1470947012971_15143" class="yiv6902441652gmail_extra"><br clear="all"><div id="yui_3_16_0_ym19_1_1470947012971_15145"><div id="yui_3_16_0_ym19_1_1470947012971_15144" class="yiv6902441652gmail_signature">----<br clear="none">Pozdrawiam, | Best regards,<br clear="none">Maciek Wójcikowski<br clear="none"><a rel="nofollow" shape="rect" ymailto="mailto:maciek@wojcikowski.pl" target="_blank" href="mailto:maciek@wojcikowski.pl">maciek@wojcikowski.pl</a><br clear="none"></div></div>
<br clear="none"><div id="yui_3_16_0_ym19_1_1470947012971_15199" class="yiv6902441652gmail_quote">2016-08-11 23:39 GMT+02:00 Ali Zude via scikit-learn <span dir="ltr"><<a rel="nofollow" shape="rect" ymailto="mailto:scikit-learn@python.org" target="_blank" href="mailto:scikit-learn@python.org">scikit-learn@python.org</a>></span>:<br clear="none"><blockquote id="yui_3_16_0_ym19_1_1470947012971_15198" class="yiv6902441652gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><div class="yiv6902441652yqt1434543815" id="yiv6902441652yqt22817"><div id="yui_3_16_0_ym19_1_1470947012971_15197"><div id="yui_3_16_0_ym19_1_1470947012971_15196" style="color:#000;background-color:#fff;font-family:times new roman, new york, times, serif;font-size:13px;"><div>Dear All,</div><div><br clear="none"></div><div dir="ltr">I am trying to speed up the prediction of Random Forests. I've used compiledtress, which was useful, but since I have 6 models and once I've loaded all of them I got "Multiprocessing exception:" <br clear="none"></div><div><br clear="none"></div><div dir="ltr">here is my models in the code:<br clear="none"></div><div id="yui_3_16_0_ym19_1_1470947012971_15195"><span style="background-color:rgb(173,215,115);">...</span>
</div><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model1=joblib.load('/models/ model1.pkl'')</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model2=joblib.load('/models/ model2.pkl')</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model3=joblib.load('/models/ model3.pkl')</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model4=compiledtrees. CompiledRegressionPredictor( joblib.load('/models/model4. pkl'))</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model5=compiledtrees. CompiledRegressionPredictor( joblib.load('/models/model4. pkl'))</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model6=compiledtrees. CompiledRegressionPredictor( joblib.load('/models/model4. pkl'))</span></div><span style="background-color:rgb(173,215,115);"> </span><div id="yui_3_16_0_ym19_1_1470947012971_15200" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);"><br clear="none"></span></div><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model1=compiledtrees. CompiledRegressionPredictor( model1)</span></div><span style="background-color:rgb(173,215,115);"> </span><div style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model2=compiledtrees. CompiledRegressionPredictor( model2)</span></div><span style="background-color:rgb(173,215,115);"> </span><div dir="ltr" style="margin-top:0px;margin-bottom:0px;margin-left:0px;margin-right:0px;text-indent:0px;"><span style="background-color:rgb(173,215,115);">model3=compiledtrees. CompiledRegressionPredictor( model3)</span></div><div id="yui_3_16_0_ym19_1_1470947012971_15202"><div id="yui_3_16_0_ym19_1_1470947012971_15201"><span style="background-color:rgb(173,215,115);">....</span></div><div><br clear="none"></div><div dir="ltr">Now I'm trying to use<span> <span style="background-color:rgb(173,215,115);">MultiOutputRegressor( RandomForestRegressor())</span>,<span><span> however, I could not find any tool to do model selection, can anyone help me either to solve the first problem or the second one</span></span></span></div><div dir="ltr"><br clear="none"></div><div dir="ltr">Best regards<br clear="none"></div></div></div></div></div><br clear="none">______________________________ _________________<br clear="none">
scikit-learn mailing list<br clear="none">
<a rel="nofollow" shape="rect" ymailto="mailto:scikit-learn@python.org" target="_blank" href="mailto:scikit-learn@python.org">scikit-learn@python.org</a><br clear="none">
<a rel="nofollow" shape="rect" target="_blank" href="https://mail.python.org/mailman/listinfo/scikit-learn">https://mail.python.org/ mailman/listinfo/scikit-learn</a><br clear="none">
<br clear="none"></blockquote></div><br clear="none"></div></div></div><br><br></div> </div> </div> </div></div></body></html>