scikit learn - sklearn GridSearchCV, SelectKBest, and SVM -

i trying make classifier uses feature selection via functioni have written, golub, returns 2 np arrays selectkbest requires. want link svm classifier linear , and optimize on possible combinations of k , c. however, have tried far has not succeeded , not sure why. code follows:

 import numpy np  sklearn import cross_validation  sklearn import svm  sklearn.feature_selection import selectkbest  sklearn.pipeline import make_pipeline, pipeline  sklearn.grid_search import gridsearchcv  golub_mod import golub   class svm_golub_linear:     def __init__(self,x,y):        self.x=x        self.y=y      def golub_svm(self):        x=self.x        y=self.y        kbest=selectkbest(golub,k=1)        k_vals=np.linspace(100,1000,10,dtype=int)        k_vals=k_vals.tolist()        c_vals=[0.00001,0.0001,0.001,0.01,.1,1,10,100,1000]         clf=svm.linearsvc(penalty='l2')        steps=[('feature_selection',kbest),('svm_linear',clf)]        pipeline=make_pipeline(steps)        params=dict(feature_selection__k=k_vals,        svm_linear__c=c_vals)        best_model=gridsearchcv(pipeline,param_grid=params)        self.model=best_model.fit(x,y)        print(best_mod.best_params_)     def svmpredict(self,x_n):        y_vals=self.model.predict(x_n)        return y_vals 

when try run this:

   model=svm_golub_linear(x,y)    model.golub_svm() 

i following error:

 typeerror: last step of chain should implement fit    '[('feature_selection',   selectkbest(k=1, score_func=<function golub @ 0x105f2c398>)), ('svm_linear', linearsvc(c=1.0, class_weight=none, dual=false, fit_intercept=true,  intercept_scaling=1, loss='squared_hinge', max_iter=1000,  multi_class='ovr', penalty='l2', random_state=none, tol=0.0001,  verbose=0))]' (type <type 'list'>) doesn't) 

i not understand because linearsvc have fit method.

in code above if replace

    pipeline=make_pipeline(steps) 

to

    pipeline=pipeline(steps) 

the code works is.