What do you mean non deterministic? If you set the random_state of models, we try to make them deterministic. Most often, any residual variability is numerical noise that reveals statistical error bars.
G Sent from my phone. Please forgive brevity and mis spelling On Dec 13, 2016, 19:29, at 19:29, Stuart Reynolds <[email protected]> wrote: >I'd like to cache some functions to avoid rebuilding models like so: > > @cached > def train(model, dataparams): ... > > >model is an (untrained) scikit-learn object and dataparams is a dict. >The @cached annotation forms a SHA checksum out of the parameters of >the >function it annotates and returns the previously calculated function >result >if the parameters match. > >The tricky part here is reliably generating a checksum from the >parameters. >Scikit uses Python's pickle ( >http://scikit-learn.org/stable/modules/model_persistence.html) but the >pickle library is non-deterministic (same inputs to pickle.dumps yields >differing output! -- *I know*). > >So... any suggestions on how to generate checksums from models in >python? > >Thanks. >- Stuart > > >------------------------------------------------------------------------ > >_______________________________________________ >scikit-learn mailing list >[email protected] >https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
