def _run_single_set(self, param):
"""Run TCAV with provided for one set of (target, concepts).
Args:
param: parameters to run
Returns:
a dictionary of results (panda frame)
"""
bottleneck = param.bottleneck
concepts = param.concepts
target_class = param.target_class
activation_generator = param.activation_generator
alpha = param.alpha
mymodel = param.model
cav_dir = param.cav_dir
# Get acts
acts = activation_generator.process_and_load_activations(
[bottleneck], concepts + [target_class])
# Get CAVs
cav_hparams = CAV.default_hparams()
cav_hparams.alpha = alpha
cav_instance = get_or_train_cav(
concepts, bottleneck, acts, cav_dir=cav_dir,
cav_hparams=cav_hparams)
# clean up
for c in concepts:
del acts[c]
# Hypo testing
a_cav_key = CAV.cav_key(concepts, bottleneck, cav_hparams.model_type,
cav_hparams.alpha)
target_class_for_compute_tcav_score = target_class
for cav_concept in concepts:
if cav_concept is self.random_counterpart or 'random' not in cav_concept:
i_up = self.compute_tcav_score(
mymodel, target_class_for_compute_tcav_score, cav_concept,
cav_instance, acts[target_class][cav_instance.bottleneck])
val_directional_dirs = self.get_directional_dir(
mymodel, target_class_for_compute_tcav_score, cav_concept,
cav_instance, acts[target_class][cav_instance.bottleneck])
result = {'cav_key' : a_cav_key, 'cav_concept' : cav_concept,
'target_class' : target_class, 'i_up' : i_up,
'val_directional_dirs_abs_mean' :
np.mean(np.abs(val_directional_dirs)),
'val_directional_dirs_mean' :
np.mean(val_directional_dirs),
'val_directional_dirs_std' :
np.std(val_directional_dirs),
'note' : 'alpha_%s ' % (alpha),
'alpha' : alpha,
'bottleneck' : bottleneck}
del acts
return result
def test_default_hparams(self):
hparam = CAV.default_hparams()
self.assertEqual(hparam.alpha, 0.01)
self.assertEqual(hparam.model_type, 'linear')
def _run_single_set(self, params):
"""
Run TCAV with provided for one set of (target, concepts).
:param params: parameters to run
:return: a dict of results (pandas dataframe)
"""
bottleneck = params.bottleneck
concepts = params.concepts
target_class = params.target_class
activation_generator = params.activation_generator
alpha = params.alpha
black_box = params.black_box
cav_dir = params.cav_dir
tf.logging.info('running %s %s' % (target_class, concepts))
acts = activation_generator(black_box, bottlenecks,
concepts + [target_class])
cav_hparams = CAV.default_hparams()
cav_hparams.alpha = alpha
cav_instance = get_or_train_cav(concepts,
bottlenecks,
acts,
cav_dir=cav_dir,
cav_hparams=cav_hparams)
for concept in concepts:
del acts[concept]
a_cav_key = CAV.cav_key(concepts, bottlenecks, cav_hparams.model_type,
cav_hparams.alpha)
target_class_for_compute_tcav_score = target_class
for cav_concept in concepts:
if cav_concept is self.random_counterpart or 'random' not in cav_concept:
i_up = self.compute_tcav_score(
black_box, target_class_for_compute_tcav_score,
cav_concept.cav_instance,
acts[target_class][cav_instance.bottleneck])
val_dir_derivatives = self.get_dir_derivative(
black_box, target_class_for_compute_tcav_score,
cav_concept.cav_instance,
acts[target_class][cav_instance.bottleneck])
result = {
'cav_key':
a_cav_key,
'cav_concept':
cav_concept,
'target_class':
target_class,
'i_up':
i_up,
'val_dir_derivatives_abs_mean':
np.mean(np.abs(val_dir_derivatives)),
'val_dir_derivatives_mean':
np.mean(val_dir_derivatives),
'val_dir_derivatives_std':
np.std(val_dir_derivatives),
'note':
'alpha_%s' % (alpha),
'alpha':
alpha,
'bottleneck':
bottlenecks
}
del acts
return result