def explain_instance(self,
data_row,
classifier_fn,
threshold=0.95,
delta=0.1,
tau=0.15,
batch_size=100,
max_anchor_size=None,
desired_label=None,
beam_size=4,
sample_whole_instances=True,
**kwargs):
# It's possible to pass in max_anchor_size
sample_fn, mapping = self.get_sample_fn(data_row,
classifier_fn,
sample_whole_instances,
desired_label=desired_label)
exp = anchor_base.AnchorBaseBeam.anchor_beam(
sample_fn,
delta=delta,
epsilon=tau,
batch_size=batch_size,
desired_confidence=threshold,
max_anchor_size=max_anchor_size,
**kwargs)
self.add_names_to_exp(data_row, exp, mapping)
exp['instance'] = data_row
exp['prediction'] = classifier_fn(
self.encoder.transform(data_row.reshape(1, -1)))[0]
explanation = anchor_explanation.AnchorExplanation(
'tabular', exp, self.as_html)
return explanation
def explainInstance(self,
text,
classifierFn,
threshold=0.95,
delta=0.1,
tau=0.15,
batchSize=100,
useProba=False,
beamSize=4,
**kwargs):
words, positions, trueLabel, sampleFn = self.getSampleFn(
text, classifierFn, useProba=useProba)
exp = anchor_base.AnchorBaseBeam.anchor_beam(
sampleFn,
delta=delta,
epsilon=tau,
batch_size=batchSize,
desired_confidence=threshold,
stop_on_first=True,
**kwargs)
exp['names'] = [words[x] for x in exp['feature']]
exp['positions'] = [positions[x] for x in exp['feature']]
exp['instance'] = text
exp['prediction'] = trueLabel
explanation = anchor_explanation.AnchorExplanation(
'text', exp, self.as_html)
return explanation
def explain_instance(self,
text,
classifier_fn,
threshold=0.95,
delta=0.1,
tau=0.15,
batch_size=100,
use_proba=False,
beam_size=4,
pertinents_negatifs=False,
pertinents_negatifs_replace=False,
**kwargs):
# words corresponds to the different words from the target sentence, positions to their positions in the sentence
# true_labels to the label predicted by the black box and sample_fn is the function generating the matrix of perturbed sentence
words, positions, true_label, sample_fn = self.get_sample_fn(
text,
classifier_fn,
use_proba=use_proba,
pertinents_negatifs=pertinents_negatifs,
pertinents_negatifs_replace=pertinents_negatifs_replace)
# Main function that find an anchor based on the matrix of perturbed sentence
exp = anchor_base.AnchorBaseBeam.anchor_beam(
sample_fn,
delta=delta,
epsilon=tau,
batch_size=batch_size,
desired_confidence=threshold,
stop_on_first=True,
pertinents_negatifs=pertinents_negatifs,
pertinents_negatifs_replace=pertinents_negatifs_replace,
**kwargs)
if pertinents_negatifs:
exp['names'] = ['not ' + words[x] for x in exp['feature']]
elif pertinents_negatifs_replace:
exp['names'] = ['not ' + words[x] for x in exp['feature']]
else:
exp['names'] = [words[x] for x in exp['feature']]
exp['positions'] = [positions[x] for x in exp['feature']]
exp['instance'] = text
exp['prediction'] = true_label
explanation = anchor_explanation.AnchorExplanation(
'text', exp, self.as_html)
return explanation