You are welcome, and I am glad to hear that it works :). And “your" approach is
definitely the cleaner way to do it … I think you just need to be a bit careful
about the n_jobs parameter in practice, I would only set it to n_jobs=-1 in the
inner loop.
Best,
Sebastian
> On May 12, 2016, at 7:17 PM, Amita Misra <amis...@ucsc.edu> wrote:
>
> Thanks.
> Actually there were 2 people running the same experiments and the other
> person was doing as you have shown above.
> We were getting the same results but since methods were different I wanted
> to ensure that I am doing it the right way.
>
> Thanks,
> Amita
>
> On Thu, May 12, 2016 at 2:43 PM, Sebastian Raschka <se.rasc...@gmail.com>
> wrote:
> I see; that’s what I thought. At first glance, the approach (code) looks
> correct to me but I haven’ t done it this way, yet. Typically, I use a more
> “manual” approach iterating over the outer folds manually (since I typically
> use nested CV for algo selection):
>
>
> gs_est = … your gridsearch, pipeline, estimator with param grid and cv=5
> skfold = StratifiedKFold(y=y_train, n_folds=5, shuffle=True, random_state=123)
>
> for outer_train_idx, outer_valid_idx in skfold:
> gs_est.fit(X_train[outer_train_idx], y_train[outer_train_idx])
> y_pred = gs_est.predict(X_train[outer_valid_idx])
> acc = accuracy_score(y_true=y_train[outer_valid_idx],
> y_pred=y_pred)
> print(' | inner ACC %.2f%% | outer ACC %.2f%%' %
> (gs_est.best_score_ * 100, acc * 100))
> cv_scores[name].append(acc)
>
> However, it should essentially do the same thing as your code if I see it
> correctly.
>
>
> > On May 12, 2016, at 4:50 PM, Amita Misra <amis...@ucsc.edu> wrote:
> >
> > Actually I do not have an independent test set and hence I want to use it
> > as an estimate for generalization performance. Hence my classifier is fixed
> > SVM and I want to learn the parameters and also estimate an unbiased
> > performance using only one set of data.
> >
> > I wanted to ensure that my code correctly does a nested 10*5 CV and the
> > parameters are learnt on a different set and final evaluation to get the
> > predicted score is on a different set.
> >
> > Amita
> >
> >
> >
> > On Thu, May 12, 2016 at 1:24 PM, Sebastian Raschka <se.rasc...@gmail.com>
> > wrote:
> > I would say there are 2 different applications of nested CV. You could use
> > it for algorithm selection (with hyperparam tuning in the inner loop). Or,
> > you could use it as an estimate of the generalization performance (only
> > hyperparam tuning), which has been reported to be less biased than the a
> > k-fold CV estimate (Varma, S., & Simon, R. (2006). Bias in error estimation
> > when using cross-validation for model selection. BMC Bioinformatics, 7, 91.
> > http://doi.org/10.1186/1471-2105-7-91)
> >
> > By "you could use it as an estimate of the generalization performance
> > (only hyperparam tuning)” I mean as a replacement for k-fold on the
> > training set and evaluation on an independent test set.
> >
> > > On May 12, 2016, at 4:16 PM, Алексей Драль <aad...@gmail.com> wrote:
> > >
> > > Hi Amita,
> > >
> > > As far as I understand your question, you only need one CV loop to
> > > optimize your objective with scoring function provided:
> > >
> > > ===
> > > pipeline=Pipeline([('scale', preprocessing.StandardScaler()),('filter',
> > > SelectKBest(f_regression)),('svr', svm.SVR())]
> > > C_range = [0.1, 1, 10, 100]
> > > gamma_range=numpy.logspace(-2, 2, 5)
> > > param_grid=[{'svr__kernel': ['rbf'], 'svr__gamma': gamma_range,'svr__C':
> > > C_range}]
> > > grid_search = GridSearchCV(pipeline, param_grid=param_grid, cv=5,
> > > scoring=scoring_function)
> > > grid_search.fit(X_train, Y_train)
> > > ===
> > >
> > > More details about it you should be able to find in documentation:
> > > •
> > > http://scikit-learn.org/stable/modules/grid_search.html#grid-search
> > > •
> > > http://scikit-learn.org/stable/modules/grid_search.html#gridsearch-scoring
> > >
> > > 2016-05-12 17:05 GMT+01:00 Amita Misra <amis...@ucsc.edu>:
> > > Hi,
> > >
> > > I have a limited dataset and hence want to learn the parameters and also
> > > evaluate the final model.
> > > From the documents it looks that nested cross validation is the way to do
> > > it. I have the code but still I want to be sure that I am not overfitting
> > > any way.
> > >
> > > pipeline=Pipeline([('scale', preprocessing.StandardScaler()),('filter',
> > > SelectKBest(f_regression)),('svr', svm.SVR())]
> > > C_range = [0.1, 1, 10, 100]
> > > gamma_range=numpy.logspace(-2, 2, 5)
> > > param_grid=[{'svr__kernel': ['rbf'], 'svr__gamma': gamma_range,'svr__C':
> > > C_range}]
> > > grid_search = GridSearchCV(pipeline, param_grid=param_grid,cv=5)
> > > Y_pred=cross_validation.cross_val_predict(grid_search, X_train,
> > > Y_train,cv=10)
> > >
> > > correlation= numpy.ma.corrcoef(Y_train,Y_pred)[0, 1]
> > >
> > >
> > > please let me know if my understanding is correct.
> > >
> > > This is 10*5 nested cross validation. Inner folds CV over training data
> > > involves a grid search over hyperparameters and outer folds evaluate the
> > > performance.
> > >
> > >
> > >
> > > Thanks,
> > > Amita--
> > > Amita Misra
> > > Graduate Student Researcher
> > > Natural Language and Dialogue Systems Lab
> > > Baskin School of Engineering
> > > University of California Santa Cruz
> > >
> > >
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> > >
> > >
> > >
> > > --
> > > Yours sincerely,
> > > Alexey A. Dral
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> > --
> > Amita Misra
> > Graduate Student Researcher
> > Natural Language and Dialogue Systems Lab
> > Baskin School of Engineering
> > University of California Santa Cruz
> >
> > ------------------------------------------------------------------------------
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>
> --
> Amita Misra
> Graduate Student Researcher
> Natural Language and Dialogue Systems Lab
> Baskin School of Engineering
> University of California Santa Cruz
>
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