I am not that much into the multi-processing implementation in scikit-learn /
joblib, but I think this could be one issue why your mac hangs… I’d say that
it’s probably the safest approach to only set the n_jobs parameter for the
innermost object.
E.g., if you 4 processors, you said the GridSearch to 2 and a k-fold loop to
e.g., 5, I can imagine that it would blow up because you are suddenly trying to
run 10 processes on 4 processors if it makes sense!?
> On May 12, 2016, at 10:26 PM, Amita Misra <amis...@ucsc.edu> wrote:
>
> I had not thought about the n_jobs parameter, mainly because it does not run
> on my mac and the system just hangs if i use it.
> The same code runs on linux server though.
>
> I have one more clarification to seek.
> I was running it on server with this code. Would this be fine or may I move
> the n_jobs=3 to GridSearchCV
>
> grid_search = GridSearchCV(pipeline,
> param_grid=param_grid,scoring=scoringcriteria,cv=5)
> scores = cross_validation.cross_val_score(grid_search, X_train,
> Y_train,cv=cvfolds,n_jobs=3)
>
> Thanks,
> Amita
>
> On Thu, May 12, 2016 at 6:58 PM, Sebastian Raschka <se.rasc...@gmail.com>
> wrote:
> 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
> > >
> > > ------------------------------------------------------------------------------
> > > Mobile security can be enabling, not merely restricting. Employees who
> > > bring their own devices (BYOD) to work are irked by the imposition of MDM
> > > restrictions. Mobile Device Manager Plus allows you to control only the
> > > apps on BYO-devices by containerizing them, leaving personal data
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> >
> >
> >
> > --
> > Amita Misra
> > Graduate Student Researcher
> > Natural Language and Dialogue Systems Lab
> > Baskin School of Engineering
> > University of California Santa Cruz
> >
> > ------------------------------------------------------------------------------
> > Mobile security can be enabling, not merely restricting. Employees who
> > bring their own devices (BYOD) to work are irked by the imposition of MDM
> > restrictions. Mobile Device Manager Plus allows you to control only the
> > apps on BYO-devices by containerizing them, leaving personal data untouched!
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>
>
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>
>
> --
> Amita Misra
> Graduate Student Researcher
> Natural Language and Dialogue Systems Lab
> Baskin School of Engineering
> University of California Santa Cruz
>
> ------------------------------------------------------------------------------
> Mobile security can be enabling, not merely restricting. Employees who
> bring their own devices (BYOD) to work are irked by the imposition of MDM
> restrictions. Mobile Device Manager Plus allows you to control only the
> apps on BYO-devices by containerizing them, leaving personal data untouched!
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