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)
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