try:
features = features.feature_sets[args.feats]
except KeyError:
parser.error("unknown feature group: {0}".format(args.feats))
l = repeat.RepeatLearner(
liblinear.liblinearL(svm_type=0, output_probability=True))
store = Store(args.feat_store, 'r') # TODO: Do we want this read-only?
for feature in features:
spaces[feature] = store.get_Space(feature)
spaces['ebmcat'] = store.get_Space('ebmcat')
proxy = DataProxy(ALTA2012Full(), store=store)
proxy.class_space = class_space
L0_cl = []
L1_fv = []
L1_gs = None
for feat in features:
proxy.feature_spaces = feat
proxy.split_name = 'crossvalidation'
with Timer() as L0_timer:
L0_cl.append(l(proxy.featuremap.raw, proxy.classmap.raw))
print >> sys.stderr, "== training L0 for {0} took {1:.2f}s ==".format(
feat, L0_timer.elapsed)
with Timer() as L1_cv_timer:
e = Experiment(proxy, l)
class_space = 'ebmcat'
try:
features = features.feature_sets[args.feats]
except KeyError:
parser.error("unknown feature group: {0}".format(args.feats))
l = repeat.RepeatLearner(liblinear.liblinearL(svm_type=0, output_probability=True))
store = Store(args.feat_store, 'r') # TODO: Do we want this read-only?
for feature in features:
spaces[feature] = store.get_Space(feature)
spaces['ebmcat'] = store.get_Space('ebmcat')
proxy = DataProxy(ALTA2012Full(), store=store)
proxy.class_space = class_space
L0_cl = []
L1_fv = []
L1_gs = None
for feat in features:
proxy.feature_spaces = feat
proxy.split_name = 'crossvalidation'
with Timer() as L0_timer:
L0_cl.append( l(proxy.featuremap.raw, proxy.classmap.raw) )
print >>sys.stderr, "== training L0 for {0} took {1:.2f}s ==".format(feat, L0_timer.elapsed)
with Timer() as L1_cv_timer:
e = Experiment(proxy, l)
if L1_gs is None:
