245 lines
10 KiB
Python
245 lines
10 KiB
Python
# Natural Language Toolkit: Naive Bayes Classifiers
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Edward Loper <edloper@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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"""
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A classifier based on the Naive Bayes algorithm. In order to find the
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probability for a label, this algorithm first uses the Bayes rule to
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express P(label|features) in terms of P(label) and P(features|label):
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| P(label) * P(features|label)
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| P(label|features) = ------------------------------
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| P(features)
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The algorithm then makes the 'naive' assumption that all features are
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independent, given the label:
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| P(label) * P(f1|label) * ... * P(fn|label)
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| P(label|features) = --------------------------------------------
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| P(features)
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Rather than computing P(features) explicitly, the algorithm just
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calculates the numerator for each label, and normalizes them so they
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sum to one:
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| P(label) * P(f1|label) * ... * P(fn|label)
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| P(label|features) = --------------------------------------------
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| SUM[l]( P(l) * P(f1|l) * ... * P(fn|l) )
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"""
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from __future__ import print_function, unicode_literals
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from collections import defaultdict
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from nltk.probability import FreqDist, DictionaryProbDist, ELEProbDist, sum_logs
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from nltk.classify.api import ClassifierI
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##//////////////////////////////////////////////////////
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## Naive Bayes Classifier
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##//////////////////////////////////////////////////////
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class NaiveBayesClassifier(ClassifierI):
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"""
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A Naive Bayes classifier. Naive Bayes classifiers are
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paramaterized by two probability distributions:
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- P(label) gives the probability that an input will receive each
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label, given no information about the input's features.
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- P(fname=fval|label) gives the probability that a given feature
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(fname) will receive a given value (fval), given that the
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label (label).
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If the classifier encounters an input with a feature that has
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never been seen with any label, then rather than assigning a
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probability of 0 to all labels, it will ignore that feature.
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The feature value 'None' is reserved for unseen feature values;
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you generally should not use 'None' as a feature value for one of
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your own features.
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"""
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def __init__(self, label_probdist, feature_probdist):
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"""
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:param label_probdist: P(label), the probability distribution
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over labels. It is expressed as a ``ProbDistI`` whose
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samples are labels. I.e., P(label) =
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``label_probdist.prob(label)``.
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:param feature_probdist: P(fname=fval|label), the probability
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distribution for feature values, given labels. It is
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expressed as a dictionary whose keys are ``(label, fname)``
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pairs and whose values are ``ProbDistI`` objects over feature
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values. I.e., P(fname=fval|label) =
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``feature_probdist[label,fname].prob(fval)``. If a given
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``(label,fname)`` is not a key in ``feature_probdist``, then
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it is assumed that the corresponding P(fname=fval|label)
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is 0 for all values of ``fval``.
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"""
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self._label_probdist = label_probdist
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self._feature_probdist = feature_probdist
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self._labels = list(label_probdist.samples())
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def labels(self):
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return self._labels
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def classify(self, featureset):
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return self.prob_classify(featureset).max()
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def prob_classify(self, featureset):
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# Discard any feature names that we've never seen before.
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# Otherwise, we'll just assign a probability of 0 to
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# everything.
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featureset = featureset.copy()
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for fname in list(featureset.keys()):
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for label in self._labels:
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if (label, fname) in self._feature_probdist:
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break
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else:
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#print 'Ignoring unseen feature %s' % fname
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del featureset[fname]
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# Find the log probabilty of each label, given the features.
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# Start with the log probability of the label itself.
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logprob = {}
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for label in self._labels:
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logprob[label] = self._label_probdist.logprob(label)
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# Then add in the log probability of features given labels.
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for label in self._labels:
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for (fname, fval) in featureset.items():
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if (label, fname) in self._feature_probdist:
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feature_probs = self._feature_probdist[label, fname]
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logprob[label] += feature_probs.logprob(fval)
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else:
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# nb: This case will never come up if the
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# classifier was created by
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# NaiveBayesClassifier.train().
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logprob[label] += sum_logs([]) # = -INF.
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return DictionaryProbDist(logprob, normalize=True, log=True)
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def show_most_informative_features(self, n=10):
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# Determine the most relevant features, and display them.
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cpdist = self._feature_probdist
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print('Most Informative Features')
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for (fname, fval) in self.most_informative_features(n):
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def labelprob(l):
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return cpdist[l, fname].prob(fval)
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labels = sorted([l for l in self._labels
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if fval in cpdist[l, fname].samples()],
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key=labelprob)
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if len(labels) == 1:
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continue
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l0 = labels[0]
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l1 = labels[-1]
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if cpdist[l0, fname].prob(fval) == 0:
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ratio = 'INF'
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else:
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ratio = '%8.1f' % (cpdist[l1, fname].prob(fval) /
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cpdist[l0, fname].prob(fval))
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print(('%24s = %-14r %6s : %-6s = %s : 1.0' %
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(fname, fval, ("%s" % l1)[:6], ("%s" % l0)[:6], ratio)))
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def most_informative_features(self, n=100):
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"""
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Return a list of the 'most informative' features used by this
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classifier. For the purpose of this function, the
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informativeness of a feature ``(fname,fval)`` is equal to the
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highest value of P(fname=fval|label), for any label, divided by
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the lowest value of P(fname=fval|label), for any label:
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| max[ P(fname=fval|label1) / P(fname=fval|label2) ]
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"""
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if hasattr(self, '_most_informative_features'):
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return self._most_informative_features[:n]
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else:
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# The set of (fname, fval) pairs used by this classifier.
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features = set()
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# The max & min probability associated w/ each (fname, fval)
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# pair. Maps (fname,fval) -> float.
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maxprob = defaultdict(lambda: 0.0)
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minprob = defaultdict(lambda: 1.0)
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for (label, fname), probdist in self._feature_probdist.items():
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for fval in probdist.samples():
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feature = (fname, fval)
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features.add(feature)
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p = probdist.prob(fval)
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maxprob[feature] = max(p, maxprob[feature])
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minprob[feature] = min(p, minprob[feature])
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if minprob[feature] == 0:
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features.discard(feature)
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# Convert features to a list, & sort it by how informative
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# features are.
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self._most_informative_features = sorted(features,
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key=lambda feature_:
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minprob[feature_]/maxprob[feature_])
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return self._most_informative_features[:n]
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@classmethod
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def train(cls, labeled_featuresets, estimator=ELEProbDist):
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"""
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:param labeled_featuresets: A list of classified featuresets,
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i.e., a list of tuples ``(featureset, label)``.
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"""
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label_freqdist = FreqDist()
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feature_freqdist = defaultdict(FreqDist)
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feature_values = defaultdict(set)
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fnames = set()
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# Count up how many times each feature value occurred, given
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# the label and featurename.
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for featureset, label in labeled_featuresets:
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label_freqdist[label] += 1
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for fname, fval in featureset.items():
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# Increment freq(fval|label, fname)
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feature_freqdist[label, fname][fval] += 1
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# Record that fname can take the value fval.
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feature_values[fname].add(fval)
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# Keep a list of all feature names.
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fnames.add(fname)
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# If a feature didn't have a value given for an instance, then
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# we assume that it gets the implicit value 'None.' This loop
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# counts up the number of 'missing' feature values for each
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# (label,fname) pair, and increments the count of the fval
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# 'None' by that amount.
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for label in label_freqdist:
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num_samples = label_freqdist[label]
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for fname in fnames:
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count = feature_freqdist[label, fname].N()
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# Only add a None key when necessary, i.e. if there are
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# any samples with feature 'fname' missing.
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if num_samples - count > 0:
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feature_freqdist[label, fname][None] += num_samples - count
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feature_values[fname].add(None)
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# Create the P(label) distribution
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label_probdist = estimator(label_freqdist)
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# Create the P(fval|label, fname) distribution
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feature_probdist = {}
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for ((label, fname), freqdist) in feature_freqdist.items():
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probdist = estimator(freqdist, bins=len(feature_values[fname]))
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feature_probdist[label, fname] = probdist
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return cls(label_probdist, feature_probdist)
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##//////////////////////////////////////////////////////
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## Demo
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##//////////////////////////////////////////////////////
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def demo():
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from nltk.classify.util import names_demo
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classifier = names_demo(NaiveBayesClassifier.train)
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classifier.show_most_informative_features()
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if __name__ == '__main__':
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demo()
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