def get_performance(slm, decisions, preproc, comparison,
namebase=None, progkey='result_w_cleanup',
return_multi=False, ctrl=None):
if decisions is None:
decisions = np.zeros((1, 3200))
else:
decisions = np.asarray(decisions)
assert decisions.shape == (1, 3200)
if namebase is None:
namebase = 'memmap_' + str(np.random.randint(1e8))
image_features = scope.slm_memmap(
desc=slm,
X=scope.get_images('float32', preproc=preproc),
name=namebase + '_img_feat')
if return_multi:
comps = ['mult', 'sqrtabsdiff']
else:
comps = [comparison]
cmp_progs = []
for comp in comps:
sresult = screening_program(
slm_desc=slm,
preproc=preproc,
comparison=comp,
namebase=namebase,
decisions=decisions,
image_features=image_features,
ctrl=ctrl)[1][progkey]
cmp_progs.append([comp, sresult])
cmp_results = pyll.rec_eval(cmp_progs)
if return_multi:
return cmp_results
else:
return cmp_results[0][1]
def screening_program(slm_desc, decisions, comparison, preproc, namebase,
image_features=None, ctrl=None):
if image_features is None:
image_features = scope.slm_memmap(
desc=slm_desc,
X=scope.get_images('float32', preproc=preproc),
name=namebase + '_img_feat')
# XXX: check that float32 images lead to correct features
# XXX: make sure namebase takes a different value for each process
# because it's important that the memmaps don't interfere on disk
def pairs_dataset(split):
return scope.pairs_memmap(
scope.verification_pairs(split=split),
image_features,
comparison_name=comparison,
name=namebase + '_pairs_' + split,
)
result = {}
train_X, train_y = pairs_dataset('DevTrain')
test_X, test_y = pairs_dataset('DevTest')
train_d = scope.get_decisions_lfw('DevTrain', decisions)
test_d = scope.get_decisions_lfw('DevTest', decisions)
train_Xyd_n, test_Xyd_n = scope.normalize_Xcols(
(train_X, train_y, train_d,),
(test_X, test_y, test_d,))
if 0 and ctrl is not None:
print >> sys.stderr, "SKIPPING FEATURE KERNEL"
train_Xyd_n = scope.attach_feature_kernels(train_Xyd_n, test_Xyd_n,
ctrl, comparison)
### TODO: put consts in config, possibly loop over them in MultiBandit
svm = scope.train_svm(train_Xyd_n,
l2_regularization=1e-3,
max_observations=20000)
new_d_train = scope.svm_decisions_lfw(svm, train_Xyd_n)
new_d_test = scope.svm_decisions_lfw(svm, test_Xyd_n)
result = scope.result_binary_classifier_stats_lfw(
train_Xyd_n,
test_Xyd_n,
new_d_train,
new_d_test,
result=result)
result_w_cleanup = scope.run_all(
result,
scope.delete_memmap(train_X),
scope.delete_memmap(test_X),
scope.delete_memmap(image_features),
)[0]
return result_w_cleanup, locals()
