[scikit-learn] using a predictor as transformer

[Lefevre, Augustin]

Hi all,

I am playing with the pipeline features of sklearn and it seems that I can't use a prediction algorithm as intermediate step.
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.

But I get a : "TypeError: All estimators should implement fit and transform."

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 ?

Best regards


X=np.hstack((np.random.randn(500,10),np.random.randint(0,10,(500,10)))) # regressor variables
y=np.random.randn(500) # target variable
ct_get = FunctionTransformer(lambda d:d[0:10]) # transformer to extract continuous variables
dt_get = FunctionTransformer(lambda d:d[11:20]) # transformer to extract discrete variables

# first step is a regression pipeline
reg = Pipeline([('ct_vars',ct_get),('scaler',StandardScaler()),('poly',PolynomialFeatures(degree=3)),('lasso',Lasso())])
# A random forest feeds on the discrete part of the data + one continuous variable
estimator = Pipeline([('level1',FeatureUnion([('dt_vars',dt_get),('reg',reg)])),('rf',RandomForestRegressor())])

estimator.fit(X,y)
print "R^2 score is :",estimator.score(X,y)



Augustin LEFEVRE| Consultant Senior | Ykems | -
 M : +33 7 77 97 94 89 | alefevre at ykems.com<mailto:alefevre at ykems.com> | www.ykems.com<http://www.ykems.com/>

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