def decode(phrasebook, mode, decoder, attach, relate=None):
"""
Decode every instance in the attachment table (predicting
relations too if we have the data/model for it).
Return the predictions made
:type attach: DataAndModel
:type relate: DataAndModel
"""
# TODO issue #9: check that call to learner can be uniform
# with 2 parameters (as logistic), as the documentation is
# inconsistent on this
if (relate is not None and mode != DecodingMode.post_label):
prob_distrib = _combine_probs(phrasebook, attach, relate)
elif is_perceptron_model(attach.model):
# home-made online models
prob_distrib = _get_attach_prob_perceptron(phrasebook, attach)
else:
# orange-based models
prob_distrib = _get_attach_prob_orange(phrasebook, attach)
# print prob_distrib
# get prediction (input is just prob_distrib)
# hence the apparent redundancy here
# TODO: issue #8: PM CHECK if works with nbest decoding
predicted = decoder.decode(prob_distrib)
if mode == DecodingMode.post_label:
predicted = [_add_labels(phrasebook, x, relate) for x in predicted]
return predicted
def _combine_single_prob(attach, relate, att, rel):
"""return the best relation for a given EDU pair and its
joint probability with the pair being attached
helper for _combine_prob
:rtype (float, string)
"""
if is_perceptron_model(attach.model):
if not attach.model.use_prob:
raise DecoderException("ERROR: Trying to output probabilities "
"while Perceptron parametrized with "
"use_prob=False!")
p_attach = attach.model.get_scores([att])[0][2]
else:
p_attach = _get_inst_attach_orange(attach.model, att)
rel_prob, best_rel = _get_inst_relate_orange(relate.model, rel)
return (p_attach * rel_prob, best_rel)
def _combine_single_prob(attach, relate, att, rel):
"""return the best relation for a given EDU pair and its
joint probability with the pair being attached
helper for _combine_prob
:rtype (float, string)
"""
if is_perceptron_model(attach.model):
if not attach.model.use_prob:
raise DecoderException("ERROR: Trying to output probabilities "
"while Perceptron parametrized with "
"use_prob=False!")
p_attach = attach.model.get_scores([att])[0][2]
else:
p_attach = _get_inst_attach_orange(attach.model, att)
rel_prob, best_rel = _get_inst_relate_orange(relate.model, rel)
return (p_attach * rel_prob, best_rel)
def decode(config, decoder, attach, relate=None):
"""
Decode every instance in the attachment table (predicting
relations too if we have the data/model for it).
Return the predictions made
TODO: check that call to learner can be uniform with 2 parameters (as
logistic), as the documentation is inconsistent on this
:type attach: DataAndModel
:type relate: DataAndModel
"""
if relate is not None and not config.post_labelling:
prob_distrib = _combine_probs(config.phrasebook,
attach, relate)
elif is_perceptron_model(attach.model):
# home-made online models
prob_distrib = _get_attach_prob_perceptron(config, attach)
else:
# orange-based models
prob_distrib = _get_attach_prob_orange(config, attach)
# print prob_distrib
# get prediction (input is just prob_distrib)
# not all decoders support the threshold keyword argument
# hence the apparent redundancy here
if config.threshold is not None:
predicted = decoder(prob_distrib,
threshold=config.threshold,
use_prob=config.use_prob)
else:
predicted = decoder(prob_distrib,
use_prob=config.use_prob)
if config.post_labelling:
predicted = _add_labels(config.phrasebook, predicted, relate)
return predicted
def decode(config, decoder, attach, relate=None):
"""
Decode every instance in the attachment table (predicting
relations too if we have the data/model for it).
Return the predictions made
TODO: check that call to learner can be uniform with 2 parameters (as
logistic), as the documentation is inconsistent on this
:type attach: DataAndModel
:type relate: DataAndModel
"""
if relate is not None and not config.post_labelling:
prob_distrib = _combine_probs(config.phrasebook, attach, relate)
elif is_perceptron_model(attach.model):
# home-made online models
prob_distrib = _get_attach_prob_perceptron(config, attach)
else:
# orange-based models
prob_distrib = _get_attach_prob_orange(config, attach)
# print prob_distrib
# get prediction (input is just prob_distrib)
# not all decoders support the threshold keyword argument
# hence the apparent redundancy here
if config.threshold is not None:
predicted = decoder(prob_distrib,
threshold=config.threshold,
use_prob=config.use_prob)
else:
predicted = decoder(prob_distrib, use_prob=config.use_prob)
if config.post_labelling:
predicted = _add_labels(config.phrasebook, predicted, relate)
return predicted
def decode(phrasebook, mode, decoder, attach,
relate=None):
"""
Decode every instance in the attachment table (predicting
relations too if we have the data/model for it).
Return the predictions made
:type attach: DataAndModel
:type relate: DataAndModel
"""
# TODO issue #9: check that call to learner can be uniform
# with 2 parameters (as logistic), as the documentation is
# inconsistent on this
if (relate is not None and
mode != DecodingMode.post_label):
prob_distrib = _combine_probs(phrasebook,
attach, relate)
elif is_perceptron_model(attach.model):
# home-made online models
prob_distrib = _get_attach_prob_perceptron(phrasebook, attach)
else:
# orange-based models
prob_distrib = _get_attach_prob_orange(phrasebook, attach)
# print prob_distrib
# get prediction (input is just prob_distrib)
# hence the apparent redundancy here
# TODO: issue #8: PM CHECK if works with nbest decoding
predicted = decoder.decode(prob_distrib)
if mode == DecodingMode.post_label:
predicted = [_add_labels(phrasebook, x, relate)
for x in predicted]
return predicted
