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<p class="MsoNormal">Hi all,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span lang="EN-US">I am playing with the pipeline features of sklearn and it seems that I can’t use a prediction algorithm as intermediate step.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">For instance, in the example below I use the output of a lasso as an additional feature to feed a random forest, in such a way that feature selection the Lasso does some preliminary feature selection.<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;background:#FFE4FF;mso-fareast-language:FR"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">But I get a : “TypeError: All estimators should implement fit and transform.”<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;background:#FFE4FF;mso-fareast-language:FR"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">So I would like to add a transform method to the Lasso estimator so that it can be used in a FeatureUnion. Is that possible ?<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US">Best regards<o:p></o:p></span></p>
<p class="MsoNormal" style="background:white"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;background:#FFE4FF;mso-fareast-language:FR"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:white"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;background:#FFE4FF;mso-fareast-language:FR"><o:p> </o:p></span></p>
<p class="MsoNormal" style="background:#EBF5FF"><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;background:#FFE4FF;mso-fareast-language:FR">X</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">=np.hstack((np.random.randn(</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">500</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">10</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">),np.random.randint(</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">0</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">10</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,(</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">500</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">10</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">))))
</span><i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:gray;mso-fareast-language:FR"># regressor variables<br>
</span></i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">y=np.random.randn(</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">500</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">)
</span><i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:gray;mso-fareast-language:FR"># target variable<br>
</span></i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">ct_get = FunctionTransformer(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:navy;mso-fareast-language:FR">lambda
</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">d:d[</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">0</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">:</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">10</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">])
</span><i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:gray;mso-fareast-language:FR"># transformer to extract continuous variables<br>
</span></i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">dt_get = FunctionTransformer(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:navy;mso-fareast-language:FR">lambda
</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">d:d[</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">11</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">:</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">20</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">])
</span><i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:gray;mso-fareast-language:FR"># transformer to extract discrete variables<br>
<br>
# first step is a regression pipeline<br>
</span></i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">reg = Pipeline([(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'ct_vars'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,ct_get),(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'scaler'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,StandardScaler()),(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'poly'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,PolynomialFeatures(</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:#660099;mso-fareast-language:FR">degree</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">=</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:blue;mso-fareast-language:FR">3</span><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">)),(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'lasso'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,Lasso())])<br>
</span><i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:gray;mso-fareast-language:FR"># A random forest feeds on the discrete part of the data + one continuous variable<br>
</span></i><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">estimator = Pipeline([(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'level1'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,FeatureUnion([(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'dt_vars'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,dt_get),(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'reg'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,reg)])),(</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">'rf'</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,RandomForestRegressor())])<br>
<br>
estimator.fit(<span style="background:#E4E4FF">X</span>,y)<br>
</span><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:navy;mso-fareast-language:FR">print
</span></b><b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:green;mso-fareast-language:FR">"R^2 score is :"</span></b><span lang="EN-US" style="font-size:10.0pt;font-family:"Courier New";color:black;mso-fareast-language:FR">,estimator.score(<span style="background:#E4E4FF">X</span>,y)<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="color:#1F497D;mso-fareast-language:FR"><o:p> </o:p></span></p>
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<p class="MsoNormal"><b><span lang="EN-US" style="font-size:10.0pt;color:black;mso-fareast-language:FR">Augustin LEFEVRE</span></b><span lang="EN-US" style="font-size:12.0pt;color:black;mso-fareast-language:FR">|<b>
</b></span><span lang="EN-US" style="font-size:10.0pt;color:black;mso-fareast-language:FR">Consultant Senior |</span><span lang="EN-US" style="font-size:10.0pt;color:#BB9A4B;mso-fareast-language:FR"> Ykems</span><span lang="EN-US" style="font-size:10.0pt;color:black;mso-fareast-language:FR">
| - </span><span lang="EN-US" style="font-size:10.0pt;color:black;mso-fareast-language:FR"><o:p></o:p></span></p>
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