Get the number of iterations in SVR

Describe the workflow you want to enable

Hi everyone,

I am manipulating SVR objects in GridSearcheCV. I am able to access the mean_fit_time in the cv_results_, but I can’t access the number of iterations of the optimization problem.

I would like to have this information to properly set the max_iter parameter of the GridSearch.

Describe your proposed solution

I have tried the following:

from sklearn.svm import SVR
from sklearn.datasets import load_boston

# Load data
X, y = load_boston(return_X_y=True)

# Model test
model = SVR(verbose=4)
model.fit(X, y)

[LibSVM]* optimization finished, #iter = 351 obj = -3012.975812, rho = -21.172739 nSV = 499, nBSV = 431 Out[1]: SVR(gamma=1.0, verbose=4)

I am interested in getting the #iter field here. It should be available as a property of the model once fitted, and all number of iterations should appear somewhere in the cv_results_.

Also, please not that this feature should be available for all libsvm-based SVM objects: SVC, SVR, etc…

Additional context

I am running this code on: Python 3.7.3 scikit-learn 0.23.1

Thanks by advance for your support.

[NicolasHug]

I don’t know why we don’t expose n_iter (it’s possible that LibSVM makes it hard for us).

Regarding GridSearch: attributes aren’t stored in cv_results. We can’t store fitted models there because that would mean storing n_param_candidates * n_folds models, which can be prohibitively expensive in terms of memory.

You can however access the attributes of the final fitted model from gs.best_estimator_

[glemaitre]

Exposing n_iter_ would be enough for the moment.