def value(self, weights, verbose=False):
objective = 0.0
if verbose:
print "Calculating log probabilities and objective..."
log_probs = list()
for pos, (label, features) in enumerate(self.labeled_extracted_features):
log_probs.append(get_log_probs(features, weights, self.labels))
objective = -sum(log_probs[index][label] for (index, (label, _)) in enumerate(self.labeled_extracted_features))
if verbose:
print "Raw objective: %f" % objective
if verbose:
print "Applying penalty"
if self.sigma:
# Apply a penalty (e.g. smooth the results)
penalty = sum(
sum(weight ** 2 for weight in feature_weights.itervalues()) for feature_weights in weights.itervalues()
)
penalty /= 2 * self.sigma ** 2
objective += penalty
if verbose:
print "Penalized objective: %f" % objective
return objective
def value(self, weights, verbose=False):
objective = 0.0
if verbose: print "Calculating log probabilities and objective..."
log_probs = list()
for pos, (label,
features) in enumerate(self.labeled_extracted_features):
log_probs.append(get_log_probs(features, weights, self.labels))
objective = -sum(
log_probs[index][label]
for (index,
(label, _)) in enumerate(self.labeled_extracted_features))
if verbose: print "Raw objective: %f" % objective
if verbose: print "Applying penalty"
if self.sigma:
# Apply a penalty (e.g. smooth the results)
penalty = sum(
sum(weight**2 for weight in feature_weights.itervalues())
for feature_weights in weights.itervalues())
penalty /= 2 * self.sigma**2
objective += penalty
if verbose: print "Penalized objective: %f" % objective
return objective
def value_and_gradient(self, weights, verbose=False):
if weights == self.last_vg_weights:
return self.last_vg
objective = 0.0
gradient = CounterMap()
if verbose:
print "Calculating log probabilities and objective..."
# log_prob
log_probs = list()
for pos, (label, features) in enumerate(self.labeled_extracted_features):
log_probs.append(get_log_probs(features, weights, self.labels))
assert (
abs(sum(exp(log_probs[pos][label]) for label in self.labels) - 1.0) < 0.0001
), "Not a distribution: P[any | features] = %f" % (sum(exp(log_probs[pos][label]) for label in self.labels))
objective = -sum(log_prob[label] for (log_prob, (label, _)) in zip(log_probs, self.labeled_extracted_features))
if verbose:
print "Raw objective: %f" % objective
if verbose:
print "Calculating expected counts..."
expected_counts = get_expected_counts(self.labeled_extracted_features, self.labels, log_probs, CounterMap())
if verbose:
print "Calculating gradient..."
gradient = expected_counts - self.empirical_counts
if verbose:
print "Applying penalty"
# Apply a penalty (e.g. smooth the results)
if self.sigma:
penalty = 0.0
for label, feature_weights in gradient.iteritems():
for feature in feature_weights:
weight = weights[label][feature]
penalty += weight ** 2
gradient[label][feature] += weight / (self.sigma ** 2)
penalty /= 2 * self.sigma ** 2
objective += penalty
if verbose:
print "Penalized objective: %f" % objective
self.last_vg_weights = weights
self.last_vg = (objective, gradient)
return (objective, gradient)
def value_and_gradient(self, weights, verbose=False):
if weights == self.last_vg_weights:
return self.last_vg
objective = 0.0
gradient = CounterMap()
if verbose: print "Calculating log probabilities and objective..."
# log_prob
log_probs = list()
for pos, (label,
features) in enumerate(self.labeled_extracted_features):
log_probs.append(get_log_probs(features, weights, self.labels))
assert abs(
sum(exp(log_probs[pos][label]) for label in self.labels) -
1.0) < 0.0001, "Not a distribution: P[any | features] = %f" % (
sum(exp(log_probs[pos][label]) for label in self.labels))
objective = -sum(log_prob[label] for (log_prob, (
label, _)) in zip(log_probs, self.labeled_extracted_features))
if verbose: print "Raw objective: %f" % objective
if verbose: print "Calculating expected counts..."
expected_counts = get_expected_counts(self.labeled_extracted_features,
self.labels, log_probs,
CounterMap())
if verbose: print "Calculating gradient..."
gradient = expected_counts - self.empirical_counts
if verbose: print "Applying penalty"
# Apply a penalty (e.g. smooth the results)
if self.sigma:
penalty = 0.0
for label, feature_weights in gradient.iteritems():
for feature in feature_weights:
weight = weights[label][feature]
penalty += weight**2
gradient[label][feature] += (weight / (self.sigma**2))
penalty /= 2 * self.sigma**2
objective += penalty
if verbose: print "Penalized objective: %f" % objective
self.last_vg_weights = weights
self.last_vg = (objective, gradient)
return (objective, gradient)
def get_log_probabilities(self, datum_features):
return get_log_probs(datum_features, self.weights, self.labels)
def get_log_probabilities(self, datum_features):
return get_log_probs(datum_features, self.weights, self.labels)
