Hm, which version of scikit-learn are you using? Are you running this on sklearn 0.18?
Best, Sebastian > On Jan 30, 2017, at 2:48 PM, Raga Markely <raga.mark...@gmail.com> wrote: > > Hi Sebastian, > > Following up on the original question on repeated Grid Search CV, I tried to > do repeated nested loop using the followings: > N_outer=10 > N_inner=10 > scores=[] > for i in range(N_outer): > k_fold_outer = StratifiedKFold(n_splits=10,shuffle=True,random_state=i) > for j in range(N_inner): > k_fold_inner = > StratifiedKFold(n_splits=10,shuffle=True,random_state=j) > gs = GridSearchCV(estimator=pipe_svc, > param_grid=param_grid,cv=k_fold_inner) > score=cross_val_score(estimator=gs,X=X,y=y,cv=k_fold_outer) > scores.append(score) > np.mean(scores) > np.std(scores) > > But, I get the following error: TypeError: 'StratifiedKFold' object is not > iterable > > I did some trials, and the error is gone when I remove cv=k_fold_inner from > gs = ... > Could you give me some tips on what I can do? > > Thank you! > Raga > > > > On Fri, Jan 27, 2017 at 1:16 PM, Raga Markely <raga.mark...@gmail.com> wrote: > Hi Sebastian, > > Sorry, I used the wrong terms (I was referring to algo as model).. great > then, i think what i have is aligned with your workflow.. > > Thank you very much for your help! > > Have a good weekend, > Raga > > On Fri, Jan 27, 2017 at 1:01 PM, Sebastian Raschka <se.rasc...@gmail.com> > wrote: > Hi, Raga, > > sounds good, but I am wondering a bit about the order. 2) should come before > 1), right? Because model selection is basically done via hyperparam > optimization. > > Not saying that this is the optimal/right approach, but I usually do it like > this: > > 1.) algo selection via nested cv > 2.) model selection based on best algo via k-fold on whole training set > 3.) fit best algo w. best hyperparams (from 2.) to whole training set > 4.) evaluate on test set > 5.) fit classifier to whole dataset, done > > Best, > Sebastian > > > On Jan 27, 2017, at 12:49 PM, Sebastian Raschka <m...@sebastianraschka.com> > > wrote: > > > > Hi, Raga, > > > > sounds good, but I am wondering a bit about the order. 2) should come > > before 1), right? Because model selection is basically done via hyperparam > > optimization. > > > > Not saying that this is the optimal/right approach, but I usually do it > > like this: > > > > 1.) algo selection via nested cv > > 2.) model selection based on best algo via k-fold on whole training set > > 3.) fit best algo w. best hyperparams (from 2.) to whole training set > > 4.) evaluate on test set > > 5.) fit classifier to whole dataset, done > > > > Best, > > Sebastian > > > >> On Jan 27, 2017, at 10:23 AM, Raga Markely <raga.mark...@gmail.com> wrote: > >> > >> Sounds good, Sebastian.. thanks for the suggestions.. > >> > >> My dataset is relatively small (only ~35 samples), and this is the > >> workflow I have set up so far.. > >> 1. Model selection: use nested loop using > >> cross_val_score(GridSearchCV(...),...) same as shown in the scikit-learn > >> page that you provided - the results show no statistically significant > >> difference in accuracy mean +/- SD among classifiers.. this is expected as > >> the pattern is pretty obvious and simple to separate by eyes after > >> dimensionality reduction (I use pipeline of stdscaler, LDA, and > >> classifier)... so i take all of them and use voting classifier in step #3.. > >> 2. Hyperparameter optimization: use GridSearchCV to optimize > >> hyperparameters of each classifiers > >> 3. Decision Region: use the hyperparameters from step #2, fit each > >> classifier separately to the whole dataset, and use voting classifier to > >> get decision region > >> > >> This sounds reasonable? > >> > >> Thank you very much! > >> Raga > >> > >> On Thu, Jan 26, 2017 at 8:31 PM, Sebastian Raschka <se.rasc...@gmail.com> > >> wrote: > >> You are welcome! And in addition, if you select among different > >> algorithms, here are some more suggestions > >> > >> a) don’t do it based on your independent test set if this is going to your > >> final model performance estimate, or be aware that it would be overly > >> optimistic > >> b) also, it’s not the best idea to select algorithms using > >> cross-validation on the same training set that you used for model > >> selection; a more robust way would be nested CV (e.g,. > >> http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html) > >> > >> But yeah, it all depends on your dataset and size. If you have a neural > >> net that takes week to train, and if you have a large dataset anyway so > >> that you can set aside large sets for testing, I’d train on > >> train/validation splits and evaluate on the test set. And to compare e.g., > >> two networks against each other on large test sets, you could do a McNemar > >> test. > >> > >> Best, > >> Sebastian > >> > >>> On Jan 26, 2017, at 8:09 PM, Raga Markely <raga.mark...@gmail.com> wrote: > >>> > >>> Ahh.. nice.. I will use that.. thanks a lot, Sebastian! > >>> > >>> Best, > >>> Raga > >>> > >>> On Thu, Jan 26, 2017 at 6:34 PM, Sebastian Raschka <se.rasc...@gmail.com> > >>> wrote: > >>> Hi, Raga, > >>> > >>> I think that if GridSearchCV is used for classification, the stratified > >>> k-fold doesn’t do shuffling by default. > >>> > >>> Say you do 20 grid search repetitions, you could then do sth like: > >>> > >>> > >>> from sklearn.model_selection import StratifiedKFold > >>> > >>> for i in range(n_reps): > >>> k_fold = StratifiedKFold(n_splits=5, shuffle=True, random_state=i) > >>> gs = GridSearchCV(..., cv=k_fold) > >>> ... > >>> > >>> Best, > >>> Sebastian > >>> > >>>> On Jan 26, 2017, at 5:39 PM, Raga Markely <raga.mark...@gmail.com> wrote: > >>>> > >>>> Hello, > >>>> > >>>> I was trying to do repeated Grid Search CV (20 repeats). I thought that > >>>> each time I call GridSearchCV, the training and test sets separated in > >>>> different splits would be different. > >>>> > >>>> However, I got the same best_params_ and best_scores_ for all 20 > >>>> repeats. It looks like the training and test sets are separated in > >>>> identical folds in each run? Just to clarify, e.g. I have the following > >>>> data: 0,1,2,3,4. Class 1 = [0,1,2] and Class 2 = [3,4]. Suppose I call > >>>> cv = 2. The split is always for instance [0,3] [1,2,4] in each repeat, > >>>> and I couldn't get [1,3] [0,2,4] or other combinations. > >>>> > >>>> If I understand correctly, GridSearchCV uses StratifiedKFold when I > >>>> enter cv = integer. The StratifiedKFold command has random state; I > >>>> wonder if there is anyway I can make the the training and test sets > >>>> randomly separated each time I call the GridSearchCV? > >>>> > >>>> Just a note, I used the following classifiers: Logistic Regression, KNN, > >>>> SVC, Kernel SVC, Random Forest, and had the same observation regardless > >>>> of the classifiers. > >>>> > >>>> Thank you very much! > >>>> Raga > >>>> > >>>> _______________________________________________ > >>>> scikit-learn mailing list > >>>> scikit-learn@python.org > >>>> https://mail.python.org/mailman/listinfo/scikit-learn > >>> > >>> _______________________________________________ > >>> scikit-learn mailing list > >>> scikit-learn@python.org > >>> https://mail.python.org/mailman/listinfo/scikit-learn > >>> > >>> _______________________________________________ > >>> scikit-learn mailing list > >>> scikit-learn@python.org > >>> https://mail.python.org/mailman/listinfo/scikit-learn > >> > >> _______________________________________________ > >> scikit-learn mailing list > >> scikit-learn@python.org > >> https://mail.python.org/mailman/listinfo/scikit-learn > >> > >> _______________________________________________ > >> scikit-learn mailing list > >> scikit-learn@python.org > >> https://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
