Currently BaseSearchCV expects a predetermined sequence of parameter
settings, which is unideal for some cases. SciPy opts for a callback
approach. I've not used that interface, but I gather something like this
might work:
class MinimizeCV(BaseEstimator):
def __init__(self, minimiser, clf, param_init, scoring, cv,
minimise_kwargs={}):
self.clf = clf
self.param_init = param_init
self.scoring = scoring
self.cv = cv
self.minimiser = minimiser
self.minimise_kwargs = minimise_kwargs
def fit(self, X, y=None):
def objective(param_values):
""""""
# TODO: parallelise fold fitting
return aggregate(
fit_grid_point(X, y, self.clf, dict(zip(param_list,
param_values)), self.scoring, ...)
for train, test in self.cv)
res = self.minimiser(objective, X0=[v for k, v in
sorted(self.param_init.iteritems())], **self.minimise_kwargs)
# TODO: store results and perhaps search history
I think a variant could be implemented that shares most of its code with
the existing BaseSearchCV.
I haven't looked at hyperopt's interface yet.
- Joel
> On Sun, Apr 7, 2013 at 6:35 PM, Roman Sinayev <[email protected]> wrote:
> >
> > It seems like brute force Grid Search takes forever when attempting to
> > determine best parameters with many classifiers. Let's say the
> > parameter space looks something like this
> > http://i.imgur.com/AiBl8Wt.png . Why not use the SciPy simulated
> > annealing or some simple genetic algorithm instead of searching
> > through all the possible parameter space of every classifier?
>
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