[scikit-learn] [GridSearchCV] Reduction of elapsed time at the second interation

[Pedro Cardoso]

Hello fellows,

i am knew at slkearn and I have a question about GridSearchCV:

I am running the following code  at a jupyter notebook :

----------------------*code*-------------------------------

opt_models = dict()
for feature in [features1, features2, features3, features4]:
    cmb = CMB(x_train, y_train, x_test, y_test, feature)
    cmb.fit()
    cmb.predict()
    opt_models[str(feature)]=cmb.get_best_model()

-------------------------------------------------------

The CMB class is just a class that contains different classification models
(SVC, decision tree, etc...). When cmb.fit() is running, a gridSearchCV is
performed at the SVC model  (which is within the cmb instance) in order to
tune the hyperparameters C, gamma, and kernel. The SCV model is implemented
using the sklearn.svm.SVC class. Here is the output of the first and second
iteration of the for loop:

---------------------*output*-------------------------------------
-> 1st iteration


Fitting 5 folds for each of 12 candidates, totalling 60 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    6.1s
[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    6.1s
[Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    6.1s
[Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done   6 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done   7 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done  11 tasks      | elapsed:    6.2s
[Parallel(n_jobs=-1)]: Done  12 tasks      | elapsed:    6.3s
[Parallel(n_jobs=-1)]: Done  13 tasks      | elapsed:    6.3s
[Parallel(n_jobs=-1)]: Done  14 tasks      | elapsed:    6.3s
[Parallel(n_jobs=-1)]: Done  15 tasks      | elapsed:    6.4s
[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    6.4s
[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:    6.4s
[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:    6.4s
[Parallel(n_jobs=-1)]: Done  19 tasks      | elapsed:    6.5s
[Parallel(n_jobs=-1)]: Done  20 tasks      | elapsed:    6.5s
[Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed:    6.5s
[Parallel(n_jobs=-1)]: Done  22 tasks      | elapsed:    6.6s
[Parallel(n_jobs=-1)]: Done  23 tasks      | elapsed:    6.7s
[Parallel(n_jobs=-1)]: Done  24 tasks      | elapsed:    6.7s
[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    6.7s
[Parallel(n_jobs=-1)]: Done  26 tasks      | elapsed:    6.8s
[Parallel(n_jobs=-1)]: Done  27 tasks      | elapsed:    6.8s
[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    6.9s
[Parallel(n_jobs=-1)]: Done  29 tasks      | elapsed:    6.9s
[Parallel(n_jobs=-1)]: Done  30 tasks      | elapsed:    6.9s
[Parallel(n_jobs=-1)]: Done  31 tasks      | elapsed:    7.0s
[Parallel(n_jobs=-1)]: Done  32 tasks      | elapsed:    7.0s
[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:    7.0s
[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    7.0s
[Parallel(n_jobs=-1)]: Done  35 tasks      | elapsed:    7.1s
[Parallel(n_jobs=-1)]: Done  36 tasks      | elapsed:    7.1s
[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:    7.2s
[Parallel(n_jobs=-1)]: Done  38 tasks      | elapsed:    7.2s
[Parallel(n_jobs=-1)]: Done  39 tasks      | elapsed:    7.2s
[Parallel(n_jobs=-1)]: Done  40 tasks      | elapsed:    7.2s
[Parallel(n_jobs=-1)]: Done  41 tasks      | elapsed:    7.3s
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    7.3s
[Parallel(n_jobs=-1)]: Done  43 tasks      | elapsed:    7.3s
[Parallel(n_jobs=-1)]: Done  44 tasks      | elapsed:    7.4s
[Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    7.4s
[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:    7.5s


-> 2nd iteration

Fitting 5 folds for each of 12 candidates, totalling 60 fits

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-1)]: Batch computation too fast (0.0260s.) Setting
batch_size=14.
[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    0.7s finished

---------------------------------------------------------------------------------------------------------------------


As you can see, the first iteration gets a elapsed time much larger than
the 2nd iteration. Does it make sense? I am afraid that the model  is doing
some kind of cache or shortcut from the 1st iteration, and consequently
could decrease the model training/performance? I already read the sklearn
documentation and I didn't saw any warning/note about this kind of
behaviour.

Thank you very much for your time :)
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