class NaiveBayesClassifier(object):
def __init__(self):
self.label_prob = ProbDist()
self.label_feat_prob = defaultdict(ProbDist)
self._fmap = FeatureMap()
def print_features(self, label):
for fid in self._fmap.keys():
if (label, fid) in self.label_feat_prob:
pdist = self.label_feat_prob[label, fid]
for val in pdist.keys():
print "{0} {1} {2} {3}".format(label, fid, val,
pdist.prob(val))
def train(self, label_features_set):
label_freq_map = FrequencyMap()
label_fid_freq_map = defaultdict(FrequencyMap)
for label, features in label_features_set:
# Count how often the label occurs.
label_freq_map.inc(label)
# Count how often a value for a given feature and label occurs.
for f, val in features.iteritems():
label_fid_freq_map[label, f].inc(val)
# Record that we have seen that feature (for some label).
self._fmap.get(f)
for label in label_freq_map.keys():
num_count = label_freq_map.freq(label)
for f in self._fmap.keys():
count = label_fid_freq_map[label, f].total()
label_fid_freq_map[label, f].inc(None, num_count - count)
# Compute the probabilities.
for label in label_freq_map.keys():
# P(label)
probability = float(label_freq_map.freq(label))/label_freq_map.total()
self.label_prob.set(label, probability)
for ((label, f), freqmap) in label_fid_freq_map.items():
for val in freqmap.keys():
p = float(freqmap.freq(val)) / float(freqmap.total())
self.label_feat_prob[label, f].set(val, p)
def classify(self, features):
features_copy = features.copy()
for f in features_copy.keys():
if f not in self._fmap.keys():
del features_copy[f]
prob_dist = ProbDist()
for label in self.label_prob.keys():
prob_dist.set(label, self.label_prob.logprob(label))
for feat, val in features_copy.iteritems():
if (label, feat) not in self.label_feat_prob:
print "ERROR"
p_dist = self.label_feat_prob[label, feat]
prob_dist.inc(label, p_dist.logprob(val))
return prob_dist
def classify(self, features):
features_copy = features.copy()
for f in features_copy.keys():
if f not in self._fmap.keys():
del features_copy[f]
prob_dist = ProbDist()
for label in self.label_prob.keys():
prob_dist.set(label, self.label_prob.logprob(label))
for feat, val in features_copy.iteritems():
if (label, feat) not in self.label_feat_prob:
print "ERROR"
p_dist = self.label_feat_prob[label, feat]
prob_dist.inc(label, p_dist.logprob(val))
return prob_dist
def __init__(self):
self.label_prob = ProbDist()
self.label_feat_prob = defaultdict(ProbDist)
self._fmap = FeatureMap()
