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# Natural Language Toolkit - OneR
# Capable of classifying the test or gold data using the OneR algorithm
#
# Author: Sumukh Ghodke <sumukh dot ghodke at gmail dot com>
#
# URL: <http://www.nltk.org/>
# This software is distributed under GPL, for license information see LICENSE.TXT
from nltk_contrib.classifier import instances as ins, decisionstump as ds, Classifier
from nltk_contrib.classifier.exceptions import invaliddataerror as inv
class OneR(Classifier):
def __init__(self, training, attributes, klass):
Classifier.__init__(self, training, attributes, klass)
self.__best_decision_stump = None
def train(self):
Classifier.train(self)
self.__best_decision_stump = self.best_decision_stump(self.training)
def classify(self, instances):
for instance in instances:
instance.classified_klass = self.__best_decision_stump.klass(instance)
def best_decision_stump(self, instances, ignore_attributes = [], algorithm = 'minimum_error'):
decision_stumps = self.possible_decision_stumps(ignore_attributes, instances)
try:
return getattr(self, algorithm)(decision_stumps)
except AttributeError:
raise inv.InvalidDataError('Invalid algorithm to find the best decision stump. ' + str(algorithm) + ' is not defined.')
def possible_decision_stumps(self, ignore_attributes, instances):
"""
Returns a list of decision stumps, one for each attribute ignoring the ones present in the
ignore list. Each decision stump maintains a count of instances having particular attribute
values.
"""
decision_stumps = self.attributes.empty_decision_stumps(ignore_attributes, self.klass);
for stump in decision_stumps:
for instance in instances:
stump.update_count(instance)
return decision_stumps
def minimum_error(self, decision_stumps):
"""
Returns the decision stump with minimum error
"""
error, min_error_stump = 1, None
for decision_stump in decision_stumps:
new_error = decision_stump.error()
if new_error < error:
error = new_error
min_error_stump = decision_stump
return min_error_stump
def is_trained(self):
return self.__best_decision_stump is not None
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