def sampled_label_probability(self, label):
count = 0
for labelled_data in self.labelled_data.values():
count += len(labelled_data)
return 1.0 * len(self.labelled_data[label]) / count
def sampled_label_probabilities(self):
probabilities = {}
for label in self.labels:
probabilities[label] = self.sampled_label_probability(label)
return probabilities
train_dir = sys.argv[1]
loader = ReviewLoader()
deceptive = loader.load(train_dir + '/positive_polarity/deceptive_from_MTurk', 'deceptive') + \
loader.load(train_dir + '/negative_polarity/deceptive_from_MTurk', 'deceptive')
truthful = loader.load(train_dir + '/negative_polarity/truthful_from_Web', 'truthful') + \
loader.load(train_dir + '/positive_polarity/truthful_from_TripAdvisor', 'truthful')
deception_learner = NaiveLearner(deceptive + truthful)
positive = loader.load(train_dir + '/positive_polarity', 'positive')
negative = loader.load(train_dir + '/negative_polarity', 'negative')
negativity_learner = NaiveLearner(positive + negative)
writer = ParameterWriter('nbmodel.txt')
writer.write(deception_learner.parameters, deception_learner.sampled_label_probabilities())
writer.write(negativity_learner.parameters, negativity_learner.sampled_label_probabilities())
for labelled_data in self.labelled_data.values():
count += len(labelled_data)
return 1.0 * len(self.labelled_data[label]) / count
def sampled_label_probabilities(self):
probabilities = {}
for label in self.labels:
probabilities[label] = self.sampled_label_probability(label)
return probabilities
train_dir = sys.argv[1]
loader = ReviewLoader()
deceptive = loader.load(train_dir + '/positive_polarity/deceptive_from_MTurk', 'deceptive') + \
loader.load(train_dir + '/negative_polarity/deceptive_from_MTurk', 'deceptive')
truthful = loader.load(train_dir + '/negative_polarity/truthful_from_Web', 'truthful') + \
loader.load(train_dir + '/positive_polarity/truthful_from_TripAdvisor', 'truthful')
deception_learner = NaiveLearner(deceptive + truthful)
positive = loader.load(train_dir + '/positive_polarity', 'positive')
negative = loader.load(train_dir + '/negative_polarity', 'negative')
negativity_learner = NaiveLearner(positive + negative)
writer = ParameterWriter('nbmodel.txt')
writer.write(deception_learner.parameters,
deception_learner.sampled_label_probabilities())
writer.write(negativity_learner.parameters,
negativity_learner.sampled_label_probabilities())
