[scikit-learn] Random StratifiedKFold Grid Search CV

Sebastian Raschka se.raschka at gmail.com
Mon Jan 30 15:37:57 EST 2017 

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.markely at 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.markely at 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.raschka at 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 <mail at 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.markely at 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.raschka at 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.markely at 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.raschka at 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.markely at 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
> >>>>
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