def prob_classify(self, featureset):
# Discard any feature names that we've never seen before.
# Otherwise, we'll just assign a probability of 0 to
# everything.
featureset = featureset.copy()
for fname in list(featureset.keys()):
for label in self._labels:
if (label, fname) in self._feature_probdist:
break
else:
#print 'Ignoring unseen feature %s' % fname
del featureset[fname]
# Find the log probabilty of each label, given the features.
# Start with the log probability of the label itself.
logprob = {}
for label in self._labels:
logprob[label] = self._label_probdist.logprob(label)
# Then add in the log probability of features given labels.
for label in self._labels:
for (fname, fval) in featureset.items():
if (label, fname) in self._feature_probdist:
feature_probs = self._feature_probdist[label, fname]
logprob[label] += feature_probs.logprob(fval)
else:
# nb: This case will never come up if the
# classifier was created by
# NaiveBayesClassifier.train().
logprob[label] += sum_logs([]) # = -INF.
return DictionaryProbDist(logprob, normalize=True, log=True)
def prob_classify(self, featureset):
# Discard any feature names that we've never seen before.
# Otherwise, we'll just assign a probability of 0 to
# everything.
featureset = featureset.copy()
for fname in list(featureset.keys()):
for label in self._labels:
if (label, fname) in self._feature_probdist:
break
else:
#print 'Ignoring unseen feature %s' % fname
del featureset[fname]
# Find the log probabilty of each label, given the features.
# Start with the log probability of the label itself.
logprob = {}
for label in self._labels:
logprob[label] = self._label_probdist.logprob(label)
# Then add in the log probability of features given labels.
for label in self._labels:
for (fname, fval) in featureset.items():
if (label, fname) in self._feature_probdist:
feature_probs = self._feature_probdist[label, fname]
logprob[label] += feature_probs.logprob(fval)
else:
# nb: This case will never come up if the
# classifier was created by
# NaiveBayesClassifier.train().
logprob[label] += sum_logs([]) # = -INF.
return DictionaryProbDist(logprob, normalize=True, log=True)
def prob_classify(_classifier, featureset):
# Discard any feature names that we've never seen before.
# Otherwise, we'll just assign a probability of 0 to
# everything.
featureset = featureset.copy()
for fname in list(featureset.keys()):
for label in _classifier._labels:
if (label, fname) in _classifier._feature_probdist:
break
else:
# print 'Ignoring unseen feature %s' % fname
del featureset[fname]
# Find the log probabilty of each label, given the features.
# Start with the log probability of the label itself.
logprob = {}
prob_features = [] #{Tuple[str, float]}
for label in _classifier._labels:
logprob[label] = _classifier._label_probdist.logprob(label)
# Then add in the log probability of features given labels.
for label in _classifier._labels:
for (fname, fval) in featureset.items():
if (label, fname) in _classifier._feature_probdist:
feature_probs = _classifier._feature_probdist[label, fname]
# print(f"{fname} Label: {label}, Prop: {feature_probs.logprob(fval)}")
logprob[label] += feature_probs.logprob(fval)
if fval:
data = {
'word': fname[9:-1],
label: feature_probs.logprob(fval)
}
prob_features.append(data)
else:
# nb: This case will never come up if the
# classifier was created by
# NaiveBayesClassifier.train().
# print(f"{fname} Label[else]: {label}, Prop: {sum_logs([])}")
logprob[label] += sum_logs([]) # = -INF.
# print(f"Label: {label}, Prop: {logprob[label]}")
words_prob = {}
for item in prob_features:
if item['word'] in words_prob:
words_prob[item['word']].update(item)
else:
words_prob[item['word']] = item
words_prob = [val for (_, val) in words_prob.items()]
# print(f"prob_features: {words_prob}")
return logprob, words_prob
def log_renormalise(self, r, nr):
'''
Calculates the renormalisation factor for observed sample types.
'''
log_prob_cov = sum_logs([log(nr_, 2) + self.log_prob_measure(r_) for r_, nr_ in zip(r, nr)])
if self._prob_measure(0) < 1:
self.log_renormal = log(1 - self._prob_measure(0), 2) + log_prob_cov
self._renormal = exp(self.log_renormal)
else: # If this happens, Good-Turing smoothing is probably a bad idea...
self.log_renormal = float('-inf')
self._renormal = 0.0
def _prob_classify(self, input):
# Make a featureset of the input after tokenizing tokenized
input_tokenized_featureset = self._tokenizeInputToFeatures(
input).copy()
# Ensuring that all the feature names are valid and can be ued
for input_feature_name in input_tokenized_featureset.keys():
for label in self._labels:
if (label, input_feature_name
) in self._featureProbabilityDistribution:
break
else:
#print 'Ignoring unseen feature %s' % input_feature_name
del input_tokenized_featureset[input_feature_name]
# Start with a log probability of 0 to avoid skewing towards larger data sets
logprob = {}
for label in self._labels:
#print "in here adding labels"
logprob[label] = 0
# Add in the log probability of features given labels.
# Iterate through the labels assigned eg : location,time, noise
for label in self._labels:
# Iterate through the input feature set one by one eg "{turkey:true, bacon:true}"
for (input_feature_name,
input_feature_val) in input_tokenized_featureset.items():
# If the combination ie (location,turkey) belongs in the trainig set, add the log probability
if (label, input_feature_name
) in self._featureProbabilityDistribution:
# Assign its probability
feature_probs = self._featureProbabilityDistribution[
label, input_feature_name]
logprob[label] += feature_probs.logprob(input_feature_val)
else:
# nb: This case will never come up if the classifier was created by
# NaiveBayesClassifier.train().
logprob[label] += sum_logs([]) # = -INF.
# print out the log prob for each label before normalizing
#for key,value in self._featureProbabilityDistribution.items():
# print "key value of featureProbabilityDistribution " + str(key) + "," + str(value.freqdist() )
#print "log prob with features is " + str(logprob)
dictprobDist = DictionaryProbDist(logprob, normalize=True, log=True)
## print out the probability for each label
#for label in dictprobDist.samples():
# print label + " is probability " + str(dictprobDist.prob(label))
return dictprobDist
def log_renormalise(self, r, nr):
'''
Calculates the renormalisation factor for observed sample types.
'''
log_prob_cov = sum_logs([
log(nr_, 2) + self.log_prob_measure(r_) for r_, nr_ in zip(r, nr)
])
if self._prob_measure(0) < 1:
self.log_renormal = log(1 - self._prob_measure(0),
2) + log_prob_cov
self._renormal = exp(self.log_renormal)
else: # If this happens, Good-Turing smoothing is probably a bad idea...
self.log_renormal = float('-inf')
self._renormal = 0.0
def _prob_classify(self, input):
# Make a featureset of the input after tokenizing tokenized
input_tokenized_featureset = self._tokenizeInputToFeatures(input).copy()
# Ensuring that all the feature names are valid and can be ued
for input_feature_name in input_tokenized_featureset.keys():
for label in self._labels:
if (label, input_feature_name) in self._featureProbabilityDistribution:
break
else:
#print 'Ignoring unseen feature %s' % input_feature_name
del input_tokenized_featureset[input_feature_name]
# Start with a log probability of 0 to avoid skewing towards larger data sets
logprob = {}
for label in self._labels:
#print "in here adding labels"
logprob[label] = 0
# Add in the log probability of features given labels.
# Iterate through the labels assigned eg : location,time, noise
for label in self._labels:
# Iterate through the input feature set one by one eg "{turkey:true, bacon:true}"
for (input_feature_name, input_feature_val) in input_tokenized_featureset.items():
# If the combination ie (location,turkey) belongs in the trainig set, add the log probability
if (label, input_feature_name) in self._featureProbabilityDistribution:
# Assign its probability
feature_probs = self._featureProbabilityDistribution[label,input_feature_name]
logprob[label] += feature_probs.logprob(input_feature_val)
else:
# nb: This case will never come up if the classifier was created by
# NaiveBayesClassifier.train().
logprob[label] += sum_logs([]) # = -INF.
# print out the log prob for each label before normalizing
#for key,value in self._featureProbabilityDistribution.items():
# print "key value of featureProbabilityDistribution " + str(key) + "," + str(value.freqdist() )
#print "log prob with features is " + str(logprob)
dictprobDist = DictionaryProbDist(logprob, normalize=True, log=True)
## print out the probability for each label
#for label in dictprobDist.samples():
# print label + " is probability " + str(dictprobDist.prob(label))
return dictprobDist
def prob_classify(self, featureset):
featureset = featureset.copy()
for fname in list(featureset.keys()):
for label in self._labels:
if (label, fname) in self._feature_probdist:
break
else:
del featureset[fname]
logprob = {}
for label in self._labels:
logprob[label] = self._label_probdist.logprob(label)
for label in self._labels:
for (fname, fval) in featureset.items():
if (label, fname) in self._feature_probdist:
feature_probs = self._feature_probdist[label, fname]
logprob[label] += feature_probs.logprob(fval)
else:
logprob[label] += sum_logs([])
return DictionaryProbDist(logprob, normalize=True, log=True)
def prob_classify(self, featureset):
featureset = featureset.copy()
for fname in list(featureset.keys()):
for label in self._labels:
if (label, fname) in self._feature_probdist:
break
else:
del featureset[fname]
logprob = {}
for label in self._labels:
logprob[label] = self._label_probdist.logprob(label)
for label in self._labels:
for (fname, fval) in featureset.items():
if (label, fname) in self._feature_probdist:
feature_probs = self._feature_probdist[label,fname]
logprob[label] += feature_probs.logprob(fval)
else:
logprob[label] += sum_logs([]) # = -INF.
return DictionaryProbDist(logprob, normalize=True, log=True)
