Way to compute the value of the loss function on data for an SGDClassifier?
The above answer was too short, outdated and might result in misleading. 
Using score method could only give accuracy (it's in the BaseEstimator). If you want the loss function, you could either call private function _get_loss_function (defined in the BaseSGDClassifier). Or accessing BaseSGDClassifier.loss_functions class attribute which will give you a dict and whose entry is the callable for loss function (with default setting)
Also using sklearn.metrics might not get exact loss used for minimization (due to regularization and what to minimize, but you can hand compute anyway). The exact code for Loss function is defined in cython code (sgd_fast.pyx, you could look up the code in scikit-learn github repo)
I'm looking for a good way to plot the minimization progress. Probably will redirect stdout and parse the output. 
BTW, I'm using 0.17.1. So a update for the answer.
I'm using an SGDClassifier in combination with the partial fit method to train with lots of data. I'd like to monitor when I've achieved an acceptable level of convergence, which means I'd like to know the loss every n iterations on some data (possibly training, possibly held-out, maybe both).
I know this information is available if I pass verbose=1 in the constructor of the classifier, but I'd like to query it programmatically rather than visually. I also know I can use the score method to get accuracy, but I'd like actual loss as measured by my chosen loss function.
Does anyone know how to do this?